Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
With the wavelet transform, image of multi-angle remote sensing is decomposed into multi-resolution. With data of each resolution, we try target-based multi-stages inversion, taking the inversion result of coarse reso...With the wavelet transform, image of multi-angle remote sensing is decomposed into multi-resolution. With data of each resolution, we try target-based multi-stages inversion, taking the inversion result of coarse resolution as the prior information of the next inversion. The result gets finer and finer until the resolution of satellite observation. In this way, the target-based multi-stages inversion can be used in remote sensing inversion of large-scaled coverage. With MISR data, we inverse structure parameters of vegetation in semiarid grassland of the Inner Mongolia Autonomous Region. The result proves that this way is efficient.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two ...Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two diseases to appear simultaneously in some main production areas.It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space,conduct detailed disease severity monitoring,and scientific control.Four images on different dates were acquired from Sentinel-2,Landsat-8,and Gaofen-1 during the critical period of winter wheat,and 22 remote sensing features that characterize the wheat growth status were then calculated.Meanwhile,6 meteorological parameters that reflect the wheat phenological information were also obtained by combining the site meteorological data and spatial interpolation technology.Then,the principal components(PCs)of comprehensive remote sensing and meteorological features were extracted with principal component analysis(PCA).The PCs-based discrimination models were established to map YR and FHB damage using the random forest(RF)and backpropagation neural network(BPNN).The models’performance was verified based on the disease field truth data(57 plots during the filling period)and 5-fold cross-validation.The results revealed that the PCs obtained after PCA dimensionality reduction outperformed the initial features(IFs)from remote sensing and meteorology in discriminating between the two diseases.Compared to the IFs,the average area under the curve for both micro-average and macro-average ROC curves increased by 0.07 in the PCs-based RF models and increased by 0.16 and 0.13,respectively,in the PCs-based BPNN models.Notably,the PCs-based BPNN discrimination model emerged as the most effective,achieving an overall accuracy of 83.9%.Our proposed discrimination model for wheat YR and FHB,coupled with multi-source remote sensing images and meteorological data,overcomes the limitations of a single-sensor and single-phase remote sensing information in multiple stress discrimination in cloudy and rainy areas.It performs well in revealing the damage spatial distribution of the two diseases at a regional scale,providing a basis for detailed disease severity monitoring,and scientific prevention and control.展开更多
Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various doma...Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.展开更多
Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satell...Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.展开更多
This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By e...This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.展开更多
Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic...Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic source.Alternatively,laser interferometry with picometer-scale motional displacement detection can rapidly and precisely measure sound-induced minute vibrations on remote surfaces.Here,we demonstrate the feasibility of sound detection up to 100 kHz at remote sites with≈60 m optical path length via laser homodyne interferometry.Based on our ultrastable hertz linewidth laser with 10-15 fractional stability,our laser interferometer achieves 0.5 pm/Hz1/2 displacement sensitivity near 10 kHz,bounded only by laser frequency noise over 10 kHz.Between 140 Hz and 15 kHz,we achieve a homodyne acoustic sensing sensitivity of subnanometer/Pascal across our conversational frequency overtones.The minimal sound pressure detectable over 60 m optical path length is≈2 mPa,with dynamic ranges over 100 dB.With the demonstrated standoff picometric distance metrology,we successfully detected and reconstructed musical scores of normal conversational volumes with high fidelity.The acoustic detection via this precision laser interferometer could be applied to selective area sound sensing for remote acoustic metrology,optomechanical vibrational motion sensing,and ultrasensitive optical microphones at the laser frequency noise limits.展开更多
Multifarious regions around the world are exposed to natural hazards and disasters,each with unique characteristics.A higher frequency of extreme hydro-meteorological events,most probably related to climate change,and...Multifarious regions around the world are exposed to natural hazards and disasters,each with unique characteristics.A higher frequency of extreme hydro-meteorological events,most probably related to climate change,and an increase in vulnerable population have been addressed as potential causes of such disasters.To mitigate the consequences of these disasters,Disaster Risk Management,including hazard assessment,elements-at-risk mapping,vulnerability and risk assessment of spatial components as well as Earth Observation(EO)products and Geographic Information Systems(GIS),should be considered.Multihazard assessment entails the evaluation of relationships between various hazards,including interconnected or cascading events,as well as focusing on various levels from global to local community levels,as each level manifests particular objectives and spatial data.This paper presents an overview of the diverse types of spatial data and explores the methods applied in hazard and risk assessments,with volcanic eruptions serving as a specific example.The rapid development of scientific research and the advancement of Earth Observation satellites in recent years have revolutionized the concepts of geologists and researchers.These satellites now play an indispensable role in supporting first responders during major disasters.The coordination of satellite deployment ensures a swift response along with allowing for the timely delivery of critical images.In tandem,remote sensing technologies and geographic information systems(GIS)have emerged as essential tools for geospatial analysis.The application of remote sensing and GIS for the detection of natural disasters was examined through a review of academic papers,offering an analysis of how remote sensing is utilized to assess natural hazards and their link to climate change.展开更多
This study investigates the prediction of groundwater Storage in the Rabat-Sale-Kenitra region under climate change conditions using advanced machine learning models.A comprehensive dataset encompassing hydrological,m...This study investigates the prediction of groundwater Storage in the Rabat-Sale-Kenitra region under climate change conditions using advanced machine learning models.A comprehensive dataset encompassing hydrological,meteorological,and geological factors was meticulously curated and preprocessed for model training.Six regression models Decision Tree,Random Forest,LightGBM,CatBoost,Extreme Learning Machine(ELM),and Artificial Neural Network(ANN)were employed to predict groundwater Storage,with hyperparameters optimized via grid search.The performance of these models was rigorously evaluated using metrics such as Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and the coefficient of determination(R^(2)).Results demonstrated that the LightGBM model outperformed the others,achieving an impressive testing RMSE of 3.07 and an R^(2)of 0.9997,indicating its robustness in handling large datasets.The Extreme Learning Machine and ANN showed considerable limitations,highlighting the importance of model selection.This research underscores the critical role of advanced machine learning techniques in enhancing groundwater resource management,providing valuable insights for policymakers in developing sustainable strategies to address groundwater challenges in the face of climate variability.展开更多
Harmful algal blooms(HABs)pose a growing threat to aquatic ecosystems,human health,and economies worldwide.Climate change and nutrient enrichment have driven an increase in both the size and frequency of blooms,making...Harmful algal blooms(HABs)pose a growing threat to aquatic ecosystems,human health,and economies worldwide.Climate change and nutrient enrichment have driven an increase in both the size and frequency of blooms,making accurate observation more urgent than ever.Remote sensing has become a critical tool for monitoring HABs,yet commonly used multispectral instruments such as MODIS,Landsat,and Sentinel face major limitations.Their coarse spatial resolution prevents reliable observation in smaller inland waters,long revisit times restrict timely monitoring,and limited spectral bands reduce the ability to discriminate key pigments like chlorophyll a and phycocyanin.In contrast,hyperspectral sensors capture hundreds of narrow,contiguous bands at higher spatial,temporal,and radiometric resolutions,enabling the detection of subtle phytoplankton characteristics and bloom dynamics across diverse environments.Hyperspectral imagers mounted on unmanned aerial systems further provide near-daily data collection,making them especially effective for tracking bloom evolution.While forecasting remains an emerging application,hyperspectral observation offers the clearest path forward for improving HAB detection and monitoring.This paper reviews the advantages of hyperspectral remote sensing over multispectral approaches and argues for its adoption as an essential technology in safeguarding aquatic health and mitigating the rising global risk of HABs.展开更多
With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,s...With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,struggle to meet the demands of modern agriculture.Drone remote sensing technology,leveraging its high efficiency and flexibility,demonstrates significant potential in pest monitoring.Equipped with multispectral,hyperspectral,and thermal infrared sensors,drones can rapidly cover large agricultural fields,capturing high-resolution imagery and data to detect spectral variations in crops.This enables effective differentiation between healthy and infested plants,facilitating early pest identification and targeted control.This paper systematically reviews the current applications of drone remote sensing technology in pest monitoring by examining different sensor types and their use in monitoring major crop pests and diseases.It also discusses existing challenges,aiming to provide insights and references for future research.展开更多
Remote sensing scene image classification is a prominent research area within remote sensing.Deep learningbased methods have been extensively utilized and have shown significant advancements in this field.Recent progr...Remote sensing scene image classification is a prominent research area within remote sensing.Deep learningbased methods have been extensively utilized and have shown significant advancements in this field.Recent progress in these methods primarily focuses on enhancing feature representation capabilities to improve performance.The challenge lies in the limited spatial resolution of small-sized remote sensing images,as well as image blurring and sparse data.These factors contribute to lower accuracy in current deep learning models.Additionally,deeper networks with attention-based modules require a substantial number of network parameters,leading to high computational costs and memory usage.In this article,we introduce ERSNet,a lightweight novel attention-guided network for remote sensing scene image classification.ERSNet is constructed using a deep separable convolutional network and incorporates an attention mechanism.It utilizes spatial attention,channel attention,and channel self-attention to enhance feature representation and accuracy,while also reducing computational complexity and memory usage.Experimental results indicate that,compared to existing state-of-the-art methods,ERSNet has a significantly lower parameter count of only 1.2 M and reduced Flops.It achieves the highest classification accuracy of 99.14%on the EuroSAT dataset,demonstrating its suitability for application on mobile terminal devices.Furthermore,experimental results from the UCMerced land use dataset and the Brazilian coffee scene also confirm the strong generalization ability of this method.展开更多
Intelligent interpretation of high-resolution remote sensing imagery is a fundamental challenge in aerospace information processing.Complex ground environments such as construction and demolition(C&D)waste landfil...Intelligent interpretation of high-resolution remote sensing imagery is a fundamental challenge in aerospace information processing.Complex ground environments such as construction and demolition(C&D)waste landfills exemplify the need for robust segmentation models that can handle diverse spatial and spectral patterns.Conventional convolutional neural networks(CNNs)are limited by their local receptive fields,whereas Transformer-based architectures often lose fine spatial detail,resulting in incomplete delineation of heterogeneous remote sensing targets.To address these issues,we propose a global-local collaborative network(GLC-Net),which is designed for intelligent remote sensing image segmentation.The model integrates an efficient Transformer block to capture global dependencies and a local enhancement block to refine structural details.Furthermore,a multi-scale spatial aggregation and enhancement(MSAE)module is introduced to strengthen contextual representation and suppress background noise.Deep supervision facilitates hierarchical feature learning.Experiments on two high-resolution remote sensing datasets(Changping and Daxing)demonstrate that GLC-Net surpasses state-of-the-art baselines by 1.5%-3.2%in mean intersection over union(mIoU),while achieving superior boundary precision and semantic consistency.These results confirm that global-local collaborative modeling provides an effective pathway for intelligent remote sensing image segmentation in aerospace environmental monitoring.展开更多
Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-...Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-running and large-scale ambitious forestation projects.However,there is still a lack of year-to-year evaluation on the effects of afforestation on carbon sequestration.Satellite remote sensing provides continuous observations of vegetation dynamics and land use and land cover change,is becoming a practical tool to evaluate the changes of vegetation productivity driven by afforestation.Here,a spatially-explicit analysis was conducted by combining Moderate Resolution Imaging Spectroradiometer(MODIS)land cover and three up-to-date remote sensing gross primary productivity(GPP)datasets of China.The results showed that the generated afforestation maps have similar spatial distribution with the national forest inventory data at the provincial level.The accumulative areas of afforestation were 3.02×10^(5)km^(2)in China from 2002 to 2018,it was mainly distributed in Southwest(SW),South(Sou),Southeast(SE)and Northeast(NE)of China.Among them,SW possesses the largest afforestation sub-region,with an area of 9.38×10^(4)km^(2),accounting for 31.06%of the total.There were divergent trends of affores-tation area among different sub-regions.The southern sub-regions showed increasing trends,while the northern sub-regions showed decreasing trends.In keeping with these,the center of annual afforestation moved to the south after 2009.The southern sub-regions were the majority of the cumula-tive GPP,accounting for nearly 70%of the total.The GPP of new afforestation showed an increasing trend from 2002 to 2018,and the increasing rate was higher than existing forests.After afforestation,the GPP change of afforestation was higher than adjacent non-forest over the same period.Our work provides new evidence that afforestation of China has enhanced the carbon assimilation and will deepen our understanding of dynamics of carbon sequestration driven by afforestation.展开更多
Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relat...Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important details.Additionally,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class confusion.To address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive fusion.Specifically,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale features.We also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature fusion.Furthermore,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed segmentation.The experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen dataset.On the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.展开更多
The large-scale acquisition and widespread application of remote sensing image data have led to increasingly severe challenges in information security and privacy protection during transmission and storage.Urban remot...The large-scale acquisition and widespread application of remote sensing image data have led to increasingly severe challenges in information security and privacy protection during transmission and storage.Urban remote sensing image,characterized by complex content and well-defined structures,are particularly vulnerable to malicious attacks and information leakage.To address this issue,the author proposes an encryption method based on the enhanced single-neuron dynamical system(ESNDS).ESNDS generates highquality pseudo-random sequences with complex dynamics and intense sensitivity to initial conditions,which drive a structure of multi-stage cipher comprising permutation,ring-wise diffusion,and mask perturbation.Using representative GF-2 Panchromatic and Multispectral Scanner(PMS)urban scenes,the author conducts systematic evaluations in terms of inter-pixel correlation,information entropy,histogram uniformity,and number of pixel change rate(NPCR)/unified average changing intensity(UACI).The results demonstrate that the proposed scheme effectively resists statistical analysis,differential attacks,and known-plaintext attacks while maintaining competitive computational efficiency for high-resolution urban image.In addition,the cipher is lightweight and hardware-friendly,integrates readily with on-board and ground processing,and thus offers tangible engineering utility for real-time,large-volume remote-sensing data protection.展开更多
Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images...Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.展开更多
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
基金the National Key Basic Research Project of China (Grant No. G2000077907)the National Natural Science Foundation of China (Grant No. 40271082)
文摘With the wavelet transform, image of multi-angle remote sensing is decomposed into multi-resolution. With data of each resolution, we try target-based multi-stages inversion, taking the inversion result of coarse resolution as the prior information of the next inversion. The result gets finer and finer until the resolution of satellite observation. In this way, the target-based multi-stages inversion can be used in remote sensing inversion of large-scaled coverage. With MISR data, we inverse structure parameters of vegetation in semiarid grassland of the Inner Mongolia Autonomous Region. The result proves that this way is efficient.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
基金supported by National Key R&D Program of China(2022YFD2000100)National Natural Science Foundation of China(42401400)Zhejiang Provincial Key Research and Development Program(2023C02018).
文摘Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two diseases to appear simultaneously in some main production areas.It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space,conduct detailed disease severity monitoring,and scientific control.Four images on different dates were acquired from Sentinel-2,Landsat-8,and Gaofen-1 during the critical period of winter wheat,and 22 remote sensing features that characterize the wheat growth status were then calculated.Meanwhile,6 meteorological parameters that reflect the wheat phenological information were also obtained by combining the site meteorological data and spatial interpolation technology.Then,the principal components(PCs)of comprehensive remote sensing and meteorological features were extracted with principal component analysis(PCA).The PCs-based discrimination models were established to map YR and FHB damage using the random forest(RF)and backpropagation neural network(BPNN).The models’performance was verified based on the disease field truth data(57 plots during the filling period)and 5-fold cross-validation.The results revealed that the PCs obtained after PCA dimensionality reduction outperformed the initial features(IFs)from remote sensing and meteorology in discriminating between the two diseases.Compared to the IFs,the average area under the curve for both micro-average and macro-average ROC curves increased by 0.07 in the PCs-based RF models and increased by 0.16 and 0.13,respectively,in the PCs-based BPNN models.Notably,the PCs-based BPNN discrimination model emerged as the most effective,achieving an overall accuracy of 83.9%.Our proposed discrimination model for wheat YR and FHB,coupled with multi-source remote sensing images and meteorological data,overcomes the limitations of a single-sensor and single-phase remote sensing information in multiple stress discrimination in cloudy and rainy areas.It performs well in revealing the damage spatial distribution of the two diseases at a regional scale,providing a basis for detailed disease severity monitoring,and scientific prevention and control.
文摘Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.
文摘Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.
文摘This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.
基金supported by the Office of Naval Research(Grant Nos.N00014-16-1-2094 and N00014-24-1-2547)the Lawrence Livermore National Laboratory(Grant No.B622827)the National Science Foundation.Y.-S.J.acknowledges support from KRISS(Grant Nos.25011026 and 25011211).
文摘Acoustic detection has many applications across science and technology from medicine to imaging and communications.However,most acoustic sensors have a common limitation in that the detection must be near the acoustic source.Alternatively,laser interferometry with picometer-scale motional displacement detection can rapidly and precisely measure sound-induced minute vibrations on remote surfaces.Here,we demonstrate the feasibility of sound detection up to 100 kHz at remote sites with≈60 m optical path length via laser homodyne interferometry.Based on our ultrastable hertz linewidth laser with 10-15 fractional stability,our laser interferometer achieves 0.5 pm/Hz1/2 displacement sensitivity near 10 kHz,bounded only by laser frequency noise over 10 kHz.Between 140 Hz and 15 kHz,we achieve a homodyne acoustic sensing sensitivity of subnanometer/Pascal across our conversational frequency overtones.The minimal sound pressure detectable over 60 m optical path length is≈2 mPa,with dynamic ranges over 100 dB.With the demonstrated standoff picometric distance metrology,we successfully detected and reconstructed musical scores of normal conversational volumes with high fidelity.The acoustic detection via this precision laser interferometer could be applied to selective area sound sensing for remote acoustic metrology,optomechanical vibrational motion sensing,and ultrasensitive optical microphones at the laser frequency noise limits.
文摘Multifarious regions around the world are exposed to natural hazards and disasters,each with unique characteristics.A higher frequency of extreme hydro-meteorological events,most probably related to climate change,and an increase in vulnerable population have been addressed as potential causes of such disasters.To mitigate the consequences of these disasters,Disaster Risk Management,including hazard assessment,elements-at-risk mapping,vulnerability and risk assessment of spatial components as well as Earth Observation(EO)products and Geographic Information Systems(GIS),should be considered.Multihazard assessment entails the evaluation of relationships between various hazards,including interconnected or cascading events,as well as focusing on various levels from global to local community levels,as each level manifests particular objectives and spatial data.This paper presents an overview of the diverse types of spatial data and explores the methods applied in hazard and risk assessments,with volcanic eruptions serving as a specific example.The rapid development of scientific research and the advancement of Earth Observation satellites in recent years have revolutionized the concepts of geologists and researchers.These satellites now play an indispensable role in supporting first responders during major disasters.The coordination of satellite deployment ensures a swift response along with allowing for the timely delivery of critical images.In tandem,remote sensing technologies and geographic information systems(GIS)have emerged as essential tools for geospatial analysis.The application of remote sensing and GIS for the detection of natural disasters was examined through a review of academic papers,offering an analysis of how remote sensing is utilized to assess natural hazards and their link to climate change.
文摘This study investigates the prediction of groundwater Storage in the Rabat-Sale-Kenitra region under climate change conditions using advanced machine learning models.A comprehensive dataset encompassing hydrological,meteorological,and geological factors was meticulously curated and preprocessed for model training.Six regression models Decision Tree,Random Forest,LightGBM,CatBoost,Extreme Learning Machine(ELM),and Artificial Neural Network(ANN)were employed to predict groundwater Storage,with hyperparameters optimized via grid search.The performance of these models was rigorously evaluated using metrics such as Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and the coefficient of determination(R^(2)).Results demonstrated that the LightGBM model outperformed the others,achieving an impressive testing RMSE of 3.07 and an R^(2)of 0.9997,indicating its robustness in handling large datasets.The Extreme Learning Machine and ANN showed considerable limitations,highlighting the importance of model selection.This research underscores the critical role of advanced machine learning techniques in enhancing groundwater resource management,providing valuable insights for policymakers in developing sustainable strategies to address groundwater challenges in the face of climate variability.
文摘Harmful algal blooms(HABs)pose a growing threat to aquatic ecosystems,human health,and economies worldwide.Climate change and nutrient enrichment have driven an increase in both the size and frequency of blooms,making accurate observation more urgent than ever.Remote sensing has become a critical tool for monitoring HABs,yet commonly used multispectral instruments such as MODIS,Landsat,and Sentinel face major limitations.Their coarse spatial resolution prevents reliable observation in smaller inland waters,long revisit times restrict timely monitoring,and limited spectral bands reduce the ability to discriminate key pigments like chlorophyll a and phycocyanin.In contrast,hyperspectral sensors capture hundreds of narrow,contiguous bands at higher spatial,temporal,and radiometric resolutions,enabling the detection of subtle phytoplankton characteristics and bloom dynamics across diverse environments.Hyperspectral imagers mounted on unmanned aerial systems further provide near-daily data collection,making them especially effective for tracking bloom evolution.While forecasting remains an emerging application,hyperspectral observation offers the clearest path forward for improving HAB detection and monitoring.This paper reviews the advantages of hyperspectral remote sensing over multispectral approaches and argues for its adoption as an essential technology in safeguarding aquatic health and mitigating the rising global risk of HABs.
文摘With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,struggle to meet the demands of modern agriculture.Drone remote sensing technology,leveraging its high efficiency and flexibility,demonstrates significant potential in pest monitoring.Equipped with multispectral,hyperspectral,and thermal infrared sensors,drones can rapidly cover large agricultural fields,capturing high-resolution imagery and data to detect spectral variations in crops.This enables effective differentiation between healthy and infested plants,facilitating early pest identification and targeted control.This paper systematically reviews the current applications of drone remote sensing technology in pest monitoring by examining different sensor types and their use in monitoring major crop pests and diseases.It also discusses existing challenges,aiming to provide insights and references for future research.
文摘Remote sensing scene image classification is a prominent research area within remote sensing.Deep learningbased methods have been extensively utilized and have shown significant advancements in this field.Recent progress in these methods primarily focuses on enhancing feature representation capabilities to improve performance.The challenge lies in the limited spatial resolution of small-sized remote sensing images,as well as image blurring and sparse data.These factors contribute to lower accuracy in current deep learning models.Additionally,deeper networks with attention-based modules require a substantial number of network parameters,leading to high computational costs and memory usage.In this article,we introduce ERSNet,a lightweight novel attention-guided network for remote sensing scene image classification.ERSNet is constructed using a deep separable convolutional network and incorporates an attention mechanism.It utilizes spatial attention,channel attention,and channel self-attention to enhance feature representation and accuracy,while also reducing computational complexity and memory usage.Experimental results indicate that,compared to existing state-of-the-art methods,ERSNet has a significantly lower parameter count of only 1.2 M and reduced Flops.It achieves the highest classification accuracy of 99.14%on the EuroSAT dataset,demonstrating its suitability for application on mobile terminal devices.Furthermore,experimental results from the UCMerced land use dataset and the Brazilian coffee scene also confirm the strong generalization ability of this method.
基金supported by the“Fourteenth Five-Year”National Key R&D Program of Chi-na(No.2024YFC3906501)the New Cornerstone Sci-ence Foundation through the XPLORER PRIZE.
文摘Intelligent interpretation of high-resolution remote sensing imagery is a fundamental challenge in aerospace information processing.Complex ground environments such as construction and demolition(C&D)waste landfills exemplify the need for robust segmentation models that can handle diverse spatial and spectral patterns.Conventional convolutional neural networks(CNNs)are limited by their local receptive fields,whereas Transformer-based architectures often lose fine spatial detail,resulting in incomplete delineation of heterogeneous remote sensing targets.To address these issues,we propose a global-local collaborative network(GLC-Net),which is designed for intelligent remote sensing image segmentation.The model integrates an efficient Transformer block to capture global dependencies and a local enhancement block to refine structural details.Furthermore,a multi-scale spatial aggregation and enhancement(MSAE)module is introduced to strengthen contextual representation and suppress background noise.Deep supervision facilitates hierarchical feature learning.Experiments on two high-resolution remote sensing datasets(Changping and Daxing)demonstrate that GLC-Net surpasses state-of-the-art baselines by 1.5%-3.2%in mean intersection over union(mIoU),while achieving superior boundary precision and semantic consistency.These results confirm that global-local collaborative modeling provides an effective pathway for intelligent remote sensing image segmentation in aerospace environmental monitoring.
基金funded by the National Key Research and Development Program of China(Grant No.2020YFA0608103)the National Science Foundation of China(Grant Nos.42265012 and 31770765).
文摘Enhancing the carbon sink of terrestrial ecosystems is an essential nature-based solution to mitigate global warming and achieve the target of carbon neutrality.Over recent decades,China has launched a series of long-running and large-scale ambitious forestation projects.However,there is still a lack of year-to-year evaluation on the effects of afforestation on carbon sequestration.Satellite remote sensing provides continuous observations of vegetation dynamics and land use and land cover change,is becoming a practical tool to evaluate the changes of vegetation productivity driven by afforestation.Here,a spatially-explicit analysis was conducted by combining Moderate Resolution Imaging Spectroradiometer(MODIS)land cover and three up-to-date remote sensing gross primary productivity(GPP)datasets of China.The results showed that the generated afforestation maps have similar spatial distribution with the national forest inventory data at the provincial level.The accumulative areas of afforestation were 3.02×10^(5)km^(2)in China from 2002 to 2018,it was mainly distributed in Southwest(SW),South(Sou),Southeast(SE)and Northeast(NE)of China.Among them,SW possesses the largest afforestation sub-region,with an area of 9.38×10^(4)km^(2),accounting for 31.06%of the total.There were divergent trends of affores-tation area among different sub-regions.The southern sub-regions showed increasing trends,while the northern sub-regions showed decreasing trends.In keeping with these,the center of annual afforestation moved to the south after 2009.The southern sub-regions were the majority of the cumula-tive GPP,accounting for nearly 70%of the total.The GPP of new afforestation showed an increasing trend from 2002 to 2018,and the increasing rate was higher than existing forests.After afforestation,the GPP change of afforestation was higher than adjacent non-forest over the same period.Our work provides new evidence that afforestation of China has enhanced the carbon assimilation and will deepen our understanding of dynamics of carbon sequestration driven by afforestation.
基金funded by National Natural Science Foundation of China(61603245).
文摘Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing.Despite the success of Convolutional Neural Networks(CNNs),they often fail to capture inter-layer feature relationships and fully leverage contextual information,leading to the loss of important details.Additionally,due to significant intraclass variation and small inter-class differences in remote sensing images,CNNs may experience class confusion.To address these issues,we propose a novel Category-Guided Feature Collaborative Learning Network(CG-FCLNet),which enables fine-grained feature extraction and adaptive fusion.Specifically,we design a Feature Collaborative Learning Module(FCLM)to facilitate the tight interaction of multi-scale features.We also introduce a Scale-Aware Fusion Module(SAFM),which iteratively fuses features from different layers using a spatial attention mechanism,enabling deeper feature fusion.Furthermore,we design a Category-Guided Module(CGM)to extract category-aware information that guides feature fusion,ensuring that the fused featuresmore accurately reflect the semantic information of each category,thereby improving detailed segmentation.The experimental results show that CG-FCLNet achieves a Mean Intersection over Union(mIoU)of 83.46%,an mF1 of 90.87%,and an Overall Accuracy(OA)of 91.34% on the Vaihingen dataset.On the Potsdam dataset,it achieves a mIoU of 86.54%,an mF1 of 92.65%,and an OA of 91.29%.These results highlight the superior performance of CG-FCLNet compared to existing state-of-the-art methods.
文摘The large-scale acquisition and widespread application of remote sensing image data have led to increasingly severe challenges in information security and privacy protection during transmission and storage.Urban remote sensing image,characterized by complex content and well-defined structures,are particularly vulnerable to malicious attacks and information leakage.To address this issue,the author proposes an encryption method based on the enhanced single-neuron dynamical system(ESNDS).ESNDS generates highquality pseudo-random sequences with complex dynamics and intense sensitivity to initial conditions,which drive a structure of multi-stage cipher comprising permutation,ring-wise diffusion,and mask perturbation.Using representative GF-2 Panchromatic and Multispectral Scanner(PMS)urban scenes,the author conducts systematic evaluations in terms of inter-pixel correlation,information entropy,histogram uniformity,and number of pixel change rate(NPCR)/unified average changing intensity(UACI).The results demonstrate that the proposed scheme effectively resists statistical analysis,differential attacks,and known-plaintext attacks while maintaining competitive computational efficiency for high-resolution urban image.In addition,the cipher is lightweight and hardware-friendly,integrates readily with on-board and ground processing,and thus offers tangible engineering utility for real-time,large-volume remote-sensing data protection.
基金supported by National Natural Science Foundation of China(No.62471034)Hebei Natural Science Foundation(No.F2023105001).
文摘Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.
