Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight N...Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight Network(CCLNet),an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources.CCLNet employs a three-stage network architecture.Its key components include three modules.C3F-Convolutional Gated Linear Unit(C3F-CGLU)performs selective local feature extraction while preserving fine-grained high-frequency flame details.Context-Guided Feature Fusion Module(CGFM)replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns.Lightweight Shared Convolution with Separated Batch Normalization Detection(LSCSBD)reduces parameters through separated batch normalization while maintaining scale-specific statistics.We build TF-11K,an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset.On TF-11K,CCLNet attains 85.8%mAP@0.5,45.5%mean Average Precision(mAP)@[0.5:0.95],87.4%precision,and 79.1%recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second(GFLOPs).The ablation study confirms that each module contributes to both accuracy and efficiency.Cross-dataset evaluation on DFS yields 77.5%mAP@0.5 and 42.3%mAP@[0.5:0.95],indicating good generalization to unseen scenes.These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs.展开更多
Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultiv...Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.展开更多
The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatia...The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatial resolution than expected.In this paper,we developed the SDI point-spread function(PSF)and Image Bivariate Optimization Algorithm(SPIBOA)to improve the quality of SDI images.The bivariate optimization method smartly combines deep learning with optical system modeling.Despite the lack of information about the real image taken by SDI and the optical system function,this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data.We use the estimated PSF to conduct deconvolution correction to observed SDI images,and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones.Meanwhile,our method also significantly reduces the inherent noise in the observed SDI images.The SPIBOA has now been successfully integrated into the routine SDI data processing,providing important support for the scientific studies based on the data.The development and application of SPIBOA also paves new ways to identify astronomical telescope systems and enhance observational image quality.Some essential factors and precautions in applying the SPIBOA method are also discussed.展开更多
Multispectral imaging,which simultaneously captures the spatial and spectral information of a scene,is widely used across diverse fields,including remote sensing,biomedical imaging,and agricultural monitoring.We intro...Multispectral imaging,which simultaneously captures the spatial and spectral information of a scene,is widely used across diverse fields,including remote sensing,biomedical imaging,and agricultural monitoring.We introduce a snapshot multispectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components.Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multispectral information;this encoded image information is rapidly decoded via a deep learning-based multispectral Fourier imager network(mFIN).We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 98.25%for predicting the illumination channels at the input and achieved a robust multispectral image reconstruction on various test objects.This deep learning-powered framework achieves high-quality multispectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine,industrial quality control,and agriculture,among others.展开更多
We report a new standalone Robotic All-Sky narrowband Imager(RASI)for auroral and airglow studies.RASI has new optics and an electromechanical system,low operation and installation costs,easy deployment and fully auto...We report a new standalone Robotic All-Sky narrowband Imager(RASI)for auroral and airglow studies.RASI has new optics and an electromechanical system,low operation and installation costs,easy deployment and fully automatic features.The new optics provide an all-sky field of view with excellent image quality and sensitivity.The new electromechanical system design offers a more compact size and the capability for outdoor independent deployment.We have also developed a fully automatic data acquisition software for RASI,which is based on the perception of solar altitude and the all-sky cloud cover.In conclusion,the RASI demonstrates significant advantages over the traditional all-sky narrowband imager,and it is highly suitable for the intensity measurements of large-scale auroras and airglow distributions.展开更多
BACKGROUND Psoriasis is often first recognized by patients through online image searches.However,search engine algorithms influenced by geographic location may still produce results that predominantly feature lighter ...BACKGROUND Psoriasis is often first recognized by patients through online image searches.However,search engine algorithms influenced by geographic location may still produce results that predominantly feature lighter skin tones,regardless of the region’s majority skin type.This underrepresentation may limit recognition and delay care for people of color.AIM To examine whether search algorithms tailor region-specific results in terms of skin color for psoriasis imagery.METHODS This observational study recruited 66 participants from 18 countries who conducted image searches for“psoriasis”across various web browsers.During the meeting,a Google form was posted to record observations,and participants reported the diversity of skin tones in the first three rows of search results using a reference image depicting Fitzpatrick types.RESULTS Results showed a global bias toward lighter skin tones,with 94%of participants identifying light skin predominance in the first row and minimal representation of medium or darker skin tones in subsequent results,verified via χ^(2) analysis.Participants who observed darker or mixed skin tones typically found them further down their results.CONCLUSION There remains a significant gap in global representation of psoriasis imagery.This paper deepens the current understanding of bias in online media and pushes for further exploration of more inclusive dermatologic imagery.展开更多
Grass constitutes a vital poetic imagery in classical Chinese poetry,embodying multifaceted symbolic connotations ranging from the tenacity of life to sentiments of separation and nostalgic longing.The translation of ...Grass constitutes a vital poetic imagery in classical Chinese poetry,embodying multifaceted symbolic connotations ranging from the tenacity of life to sentiments of separation and nostalgic longing.The translation of this botanical motif necessitates not merely lexical equivalence,but more importantly,the transmission of its profound cultural resonance and aesthetic essence.This study posits that effective rendition of grass imagery should adopt an integrative approach synthesizing the objectives of cultural translation with the intrinsic aesthetic characteristics of classical poetry.Through systematic analysis of the cultural semiotics embedded in grass symbolism,the research investigates practical translation techniques at lexical,syntactic,and stylistic dimensions.The findings aim to contribute to the theoretical framework of cultural image translation in Chinese poetic tradition while providing methodological references for cross-cultural interpretation of classical verse.By bridging cultural semiotics with translation praxis,this investigation seeks to advance the intercultural communication of Chinese poetic heritage through nuanced treatment of its botanical symbolism.展开更多
With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations...With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles.This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model.By capturing both thermal and visual cues,our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes.We first established a dual-view dataset comprising infrared and visible light videos,implemented an innovative image feature fusion strategy,and designed a deep learning model based on the transformer architecture and attention mechanism for smoke classification.Experimental results demonstrate that our method outperforms existing methods,under the condition of multi-view input,it achieves an accuracy rate of 90.88%,precision rate of 98.38%,recall rate of 92.41%and false positive and false negative rates both below 5%,underlining the effectiveness of the proposed multimodal and multi-view fusion approach.The attention mechanism plays a crucial role in improving detection performance,particularly in identifying subtle smoke features.展开更多
In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study pr...In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet model.By introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global scales.This metric effectively captures the intricate noise and distortions inherent in the HSI data.Subsequently,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality bands.To validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are conducted.In a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is benchmarked.The authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands.In addition,the authors assess the classification accuracy utilising these reconstructed bands.In various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band reconstruction.Notably,the authors’approach obviates the need for manually prefiltering of noisy bands.This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.展开更多
Efficient banana crop detection is crucial for precision agriculture;however,traditional remote sensing methods often lack the spatial resolution required for accurate identification.This study utilizes low-altitude U...Efficient banana crop detection is crucial for precision agriculture;however,traditional remote sensing methods often lack the spatial resolution required for accurate identification.This study utilizes low-altitude Unmanned Aerial Vehicle(UAV)images and deep learning-based object detection models to enhance banana plant detection.A comparative analysis of Faster Region-Based Convolutional Neural Network(Faster R-CNN),You Only Look Once Version 3(YOLOv3),Retina Network(RetinaNet),and Single Shot MultiBox Detector(SSD)was conducted to evaluate their effectiveness.Results show that RetinaNet achieved the highest detection accuracy,with a precision of 96.67%,a recall of 71.67%,and an F1 score of 81.33%.The study further highlights the impact of scale variation,occlusion,and vegetation density on detection performance.Unlike previous studies,this research systematically evaluates multi-scale object detection models for banana plant identification,offering insights into the advantages of UAV-based deep learning applications in agriculture.In addition,this study compares five evaluation metrics across the four detection models using both RGB and grayscale images.Specifically,RetinaNet exhibited the best overall performance with grayscale images,achieving the highest values across all five metrics.Compared to its performance with RGB images,these results represent a marked improvement,confirming the potential of grayscale preprocessing to enhance detection capability.展开更多
Landslide hazard detection is a prevalent problem in remote sensing studies,particularly with the technological advancement of computer vision.With the continuous and exceptional growth of the computational environmen...Landslide hazard detection is a prevalent problem in remote sensing studies,particularly with the technological advancement of computer vision.With the continuous and exceptional growth of the computational environment,the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning.Furthermore,attention models,driven by human visual procedures,have become vital in natural hazard-related studies.Hence,this paper proposes an enhanced YOLOv5(You Only Look Once version 5)network for improved satellite-based landslide detection,embedded with two popular attention modules:CBAM(Convolutional Block Attention Module)and ECA(Efficient Channel Attention).These attention mechanisms are incorporated into the backbone and neck of the YOLOv5 architecture,distinctly,and evaluated across three YOLOv5 variants:nano(n),small(s),and medium(m).The experiments use opensource satellite images from three distinct regions with complex terrain.The standard metrics,including F-score,precision,recall,and mean average precision(mAP),are computed for quantitative assessment.The YOLOv5n+CBAM demonstrates the most optimal results with an F-score of 77.2%,confirming its effectiveness.The suggested attention-driven architecture augments detection accuracy,supporting post-landslide event assessment and recovery.展开更多
基金funded by the Natural Science Foundation of Hunan Province(Grant No.2025JJ80352)the National Natural Science Foundation Project of China(Grant No.32271879).
文摘Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight Network(CCLNet),an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources.CCLNet employs a three-stage network architecture.Its key components include three modules.C3F-Convolutional Gated Linear Unit(C3F-CGLU)performs selective local feature extraction while preserving fine-grained high-frequency flame details.Context-Guided Feature Fusion Module(CGFM)replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns.Lightweight Shared Convolution with Separated Batch Normalization Detection(LSCSBD)reduces parameters through separated batch normalization while maintaining scale-specific statistics.We build TF-11K,an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset.On TF-11K,CCLNet attains 85.8%mAP@0.5,45.5%mean Average Precision(mAP)@[0.5:0.95],87.4%precision,and 79.1%recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second(GFLOPs).The ablation study confirms that each module contributes to both accuracy and efficiency.Cross-dataset evaluation on DFS yields 77.5%mAP@0.5 and 42.3%mAP@[0.5:0.95],indicating good generalization to unseen scenes.These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs.
基金Under the auspices of the National Natural Science Foundation of China(No.42101342,U2243205)the Third Comprehensive Scientific Expedition to Xinjiang(No.2021XJKK1403)。
文摘Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.12233012,the Strategic Priority Research Program of the Chinese Academy of Sciences,grant No.XDB0560102the National Key R&D Program of China 2022YFF0503003(2022YFF0503000)。
文摘The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatial resolution than expected.In this paper,we developed the SDI point-spread function(PSF)and Image Bivariate Optimization Algorithm(SPIBOA)to improve the quality of SDI images.The bivariate optimization method smartly combines deep learning with optical system modeling.Despite the lack of information about the real image taken by SDI and the optical system function,this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data.We use the estimated PSF to conduct deconvolution correction to observed SDI images,and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones.Meanwhile,our method also significantly reduces the inherent noise in the observed SDI images.The SPIBOA has now been successfully integrated into the routine SDI data processing,providing important support for the scientific studies based on the data.The development and application of SPIBOA also paves new ways to identify astronomical telescope systems and enhance observational image quality.Some essential factors and precautions in applying the SPIBOA method are also discussed.
文摘Multispectral imaging,which simultaneously captures the spatial and spectral information of a scene,is widely used across diverse fields,including remote sensing,biomedical imaging,and agricultural monitoring.We introduce a snapshot multispectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components.Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multispectral information;this encoded image information is rapidly decoded via a deep learning-based multispectral Fourier imager network(mFIN).We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 98.25%for predicting the illumination channels at the input and achieved a robust multispectral image reconstruction on various test objects.This deep learning-powered framework achieves high-quality multispectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine,industrial quality control,and agriculture,among others.
基金the projects of the CAS“Light of West China”Program,the National Natural Science Foundation of China(11573067)the Science and Technology Program of Shanghai(21DZ206100)+2 种基金the Ground-based Space Environment Monitoring Network(Chinese Meridian Project II)the Yunnan Revitalization Talent Support Program-Science&Technology Champion Project(202105AB160001)the Chinese National Antarctic&Arctic Research Expedition for the support of the all-sky auroral/airglow imager program at the Longyearbyen Station and Lijiang Station.
文摘We report a new standalone Robotic All-Sky narrowband Imager(RASI)for auroral and airglow studies.RASI has new optics and an electromechanical system,low operation and installation costs,easy deployment and fully automatic features.The new optics provide an all-sky field of view with excellent image quality and sensitivity.The new electromechanical system design offers a more compact size and the capability for outdoor independent deployment.We have also developed a fully automatic data acquisition software for RASI,which is based on the perception of solar altitude and the all-sky cloud cover.In conclusion,the RASI demonstrates significant advantages over the traditional all-sky narrowband imager,and it is highly suitable for the intensity measurements of large-scale auroras and airglow distributions.
文摘BACKGROUND Psoriasis is often first recognized by patients through online image searches.However,search engine algorithms influenced by geographic location may still produce results that predominantly feature lighter skin tones,regardless of the region’s majority skin type.This underrepresentation may limit recognition and delay care for people of color.AIM To examine whether search algorithms tailor region-specific results in terms of skin color for psoriasis imagery.METHODS This observational study recruited 66 participants from 18 countries who conducted image searches for“psoriasis”across various web browsers.During the meeting,a Google form was posted to record observations,and participants reported the diversity of skin tones in the first three rows of search results using a reference image depicting Fitzpatrick types.RESULTS Results showed a global bias toward lighter skin tones,with 94%of participants identifying light skin predominance in the first row and minimal representation of medium or darker skin tones in subsequent results,verified via χ^(2) analysis.Participants who observed darker or mixed skin tones typically found them further down their results.CONCLUSION There remains a significant gap in global representation of psoriasis imagery.This paper deepens the current understanding of bias in online media and pushes for further exploration of more inclusive dermatologic imagery.
文摘Grass constitutes a vital poetic imagery in classical Chinese poetry,embodying multifaceted symbolic connotations ranging from the tenacity of life to sentiments of separation and nostalgic longing.The translation of this botanical motif necessitates not merely lexical equivalence,but more importantly,the transmission of its profound cultural resonance and aesthetic essence.This study posits that effective rendition of grass imagery should adopt an integrative approach synthesizing the objectives of cultural translation with the intrinsic aesthetic characteristics of classical poetry.Through systematic analysis of the cultural semiotics embedded in grass symbolism,the research investigates practical translation techniques at lexical,syntactic,and stylistic dimensions.The findings aim to contribute to the theoretical framework of cultural image translation in Chinese poetic tradition while providing methodological references for cross-cultural interpretation of classical verse.By bridging cultural semiotics with translation praxis,this investigation seeks to advance the intercultural communication of Chinese poetic heritage through nuanced treatment of its botanical symbolism.
基金supported by the National Natural Science Foundation of China(32171797)Chunhui Project Foundation of the Education Department of China(HZKY20220026).
文摘With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles.This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model.By capturing both thermal and visual cues,our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes.We first established a dual-view dataset comprising infrared and visible light videos,implemented an innovative image feature fusion strategy,and designed a deep learning model based on the transformer architecture and attention mechanism for smoke classification.Experimental results demonstrate that our method outperforms existing methods,under the condition of multi-view input,it achieves an accuracy rate of 90.88%,precision rate of 98.38%,recall rate of 92.41%and false positive and false negative rates both below 5%,underlining the effectiveness of the proposed multimodal and multi-view fusion approach.The attention mechanism plays a crucial role in improving detection performance,particularly in identifying subtle smoke features.
基金National Natural Science Foundation Major Project of China,Grant/Award Number:42192580Guangdong Province Key Construction Discipline Scientific Research Ability Promotion Project,Grant/Award Number:2022ZDJS015。
文摘In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet model.By introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global scales.This metric effectively captures the intricate noise and distortions inherent in the HSI data.Subsequently,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality bands.To validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are conducted.In a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is benchmarked.The authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands.In addition,the authors assess the classification accuracy utilising these reconstructed bands.In various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band reconstruction.Notably,the authors’approach obviates the need for manually prefiltering of noisy bands.This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.
文摘Efficient banana crop detection is crucial for precision agriculture;however,traditional remote sensing methods often lack the spatial resolution required for accurate identification.This study utilizes low-altitude Unmanned Aerial Vehicle(UAV)images and deep learning-based object detection models to enhance banana plant detection.A comparative analysis of Faster Region-Based Convolutional Neural Network(Faster R-CNN),You Only Look Once Version 3(YOLOv3),Retina Network(RetinaNet),and Single Shot MultiBox Detector(SSD)was conducted to evaluate their effectiveness.Results show that RetinaNet achieved the highest detection accuracy,with a precision of 96.67%,a recall of 71.67%,and an F1 score of 81.33%.The study further highlights the impact of scale variation,occlusion,and vegetation density on detection performance.Unlike previous studies,this research systematically evaluates multi-scale object detection models for banana plant identification,offering insights into the advantages of UAV-based deep learning applications in agriculture.In addition,this study compares five evaluation metrics across the four detection models using both RGB and grayscale images.Specifically,RetinaNet exhibited the best overall performance with grayscale images,achieving the highest values across all five metrics.Compared to its performance with RGB images,these results represent a marked improvement,confirming the potential of grayscale preprocessing to enhance detection capability.
基金supported by the Department of Science and Technology,Science and Engineering Research Board,New Delhi,India,under Grant No.EEQ/2022/000812.
文摘Landslide hazard detection is a prevalent problem in remote sensing studies,particularly with the technological advancement of computer vision.With the continuous and exceptional growth of the computational environment,the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning.Furthermore,attention models,driven by human visual procedures,have become vital in natural hazard-related studies.Hence,this paper proposes an enhanced YOLOv5(You Only Look Once version 5)network for improved satellite-based landslide detection,embedded with two popular attention modules:CBAM(Convolutional Block Attention Module)and ECA(Efficient Channel Attention).These attention mechanisms are incorporated into the backbone and neck of the YOLOv5 architecture,distinctly,and evaluated across three YOLOv5 variants:nano(n),small(s),and medium(m).The experiments use opensource satellite images from three distinct regions with complex terrain.The standard metrics,including F-score,precision,recall,and mean average precision(mAP),are computed for quantitative assessment.The YOLOv5n+CBAM demonstrates the most optimal results with an F-score of 77.2%,confirming its effectiveness.The suggested attention-driven architecture augments detection accuracy,supporting post-landslide event assessment and recovery.
