Sand dust belts span approximately one-fifth of the global land surface.In these regions,dust tends to settle on vegetation surfaces,altering the observed reflectance and affecting remote sensing detections.To enhance...Sand dust belts span approximately one-fifth of the global land surface.In these regions,dust tends to settle on vegetation surfaces,altering the observed reflectance and affecting remote sensing detections.To enhance the accuracy of maize growth monitoring in dust-affected regions,this study aims to quantify the effect of sand dust retention on maize during the tasseling stage in the Kashgar Prefecture,Xinjiang Uygur Autonomous Region,China,by analyzing changes in canopy reflectance and vegetation indices.First,field sampling was conducted to measure the key canopy structure parameters and dust retention levels of maize,and laboratory spectral measurements were performed on leaf spectral properties under gradient dust retention.The measured data were then used to drive the LargE-Scale remote sensing data and image Simulation framework(LESS)model for simulating realistic maize canopy spectra across different dust levels,with validation against Sentinel-2 imagery.Second,on the basis of the simulated and satellite-derived spectra,the dust resistance of 36 common vegetation indices was systematically evaluated,and new robust dust-resistant indices were developed.The results showed that compared with dust-free maize,the canopy reflectance of dust-retained maize followed an increase–decrease–increase pattern,with critical turning points at 735 and 1325 nm.The maximum reflectance difference of–0.11755(change rate:29.002%)occurred within the 735–1325 nm range at 24 g/m^(2)dust retention,and the minimum reflectance difference of 0.04285(change rate:148.950%)was observed in the 350–735 nm range under the same dust retention level.Among the 36 vegetation indices,only the global environment monitoring index(GEMI)and the ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index(TCARI/OSAVI)exhibited dust resistance,with GEMI being effective below 6 g/m^(2)and TCARI/OSAVI remaining stable across all levels(average ratio:0.970).The newly developed indices in this study,(RE3–RE2)/(NIR–RE2),(RE3–RE2)/(RE4–RE2),and(NIR–RE2)/(RE4–RE2),retained values within the predefined dust-resistant range over the full dust retention levels of 0–24 g/m^(2),thus showing a more stable dust resistance compared with the commonly used 36 vegetation indices.Specially,(RE3–RE2)/(RE4–RE2)performed the most robustly in Sentinel-2 imagery,that is,58.020%of pixels were within the dust-resistant range,and an average ratio of 0.937 was obtained for the original-spectra index.This study provides a scientific basis for crop monitoring and management in dust-affected regions.展开更多
A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV col...A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.展开更多
Microwave chips are widely utilized in modern communication,national defense,and various technological domains.However,effective signal identification remains challenging due to complex multi-frequency microwave inter...Microwave chips are widely utilized in modern communication,national defense,and various technological domains.However,effective signal identification remains challenging due to complex multi-frequency microwave interference.To address this issue,we propose an advanced optical imaging framework based on nitrogen-vacancy(NV)center near-field microscopy.This framework enables the separation and imaging characterization of mixed multi-frequency microwave signals across a wide field of view(2000μm×1600μm,spatial resolution of 5μm)on chip surfaces.By leveraging the NV color center as a mixer,combined with a multi-frequency hybrid model and fast Fourier transform(FFT)analysis,we convert the invisible electromagnetic waves into visible optical information.Using a wide-field microscopy system equipped with a high-speed optical camera,our approach effectively enables the separation and imaging of mixed microwave signals across two complex scenarios.Comparative analysis with finite element simulation validates the accuracy of this approach.Experimental results reveal m Hz frequency resolution for GHz microwaves andμT-level signal intensity resolution,showcasing its superior capability for imaging mixed signals with multi-frequency.These findings provide critical technical support for microwave chip characterization,interference signal identification,and diagnostic testing,highlighting the broad applicability of this technique.展开更多
This study introduces an effective framework for image encryption,leveraging the principles of chaos theory through the use of cellular automata neighborhood(CAN)and a novel two-dimensional hyperchaotic map(2D-SGHM)de...This study introduces an effective framework for image encryption,leveraging the principles of chaos theory through the use of cellular automata neighborhood(CAN)and a novel two-dimensional hyperchaotic map(2D-SGHM)derived from the classic sine and Gauss maps.The core of our investigation delved into the basic performance and dynamical behaviors of this map.The findings reveal a wide hyperchaotic range characterized by large positive Lyapunov exponents,establishing map superiority in image encryption.By integrating different cellular automata neighborhoods,we can generate diverse image encryption schemes.Specifically,this study highlights three distinct image encryption algorithms constructed from one-dimensional,von Neumann and Moore neighborhoods,named OIEA,NIEA,and MIEA.Each scheme is uniquely designed to harness the benefits of CAN for encryption,thereby enhancing the overall effectiveness and security level of the image encryption process.In the experiment,the number of pixels change rates for OIEA,NIEA,and MIEA are 99.6097%,99.6092%,and 99.6098%,and the unified average changing intensities are 33.4603%,33.4626%,and 33.4628%,respectively.The correlation coefficients of the neighboring pixels are notably low,recorded at 0.00065,0.00064,and 0.00031 for each scheme,while the information entropies are nearly identical,with scores of 7.9977,7.9975,and 7.9976,showing that our encryption schemes have high security performance and can provide reliable security for different types of data information.展开更多
Remote sensing image(RSI)with concurrently high spatial,temporal,and spectral resolutions cannot be produced by a single sensor.Multisource RSI fusion is a convenient technique to realize high spatial resolution multi...Remote sensing image(RSI)with concurrently high spatial,temporal,and spectral resolutions cannot be produced by a single sensor.Multisource RSI fusion is a convenient technique to realize high spatial resolution multispectral(MS)images(spatial spectral fusion,i.e.SSF)and high temporal and spatial resolution MS images(spatiotemporal fusion,i.e.STF).Currently,deep learning-based fusion models can only implement SSF or STF,lacking models that perform both SSF and STF.Multiresolution generative adversarial networks with bidirectional adaptive-stage progressive guided fusion(BAPGF)for RSI are proposed to implement both SSF and STF,namely BPF-MGAN.A bidirectional adaptive-stage feature extraction architecture infine-scale-to-coarse-scale and coarse-scale-to-fine-scale modes is introduced.The designed BAPGF introduces a previous fusion result-oriented cross-stage-level dual-residual attention fusion strategy to enhance critical information and suppress superfluous information.Adaptive resolution U-shaped discriminators are implemented to feed multiresolution context into the generator.A generalized multitask loss function unlimited by no-reference images is developed to strengthen the model via constraints on the multiscale feature,structural,and content similarities.The BPF-MGAN model is validated on SSF datasets and STF datasets.Compared with the state-of-the-art SSF and STF models,results demonstrate the superior performance of the proposed BPF-MGAN model in both subjective and objective evaluations.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(N2001020)the National Natural Science Foundation of China(41201359).
文摘Sand dust belts span approximately one-fifth of the global land surface.In these regions,dust tends to settle on vegetation surfaces,altering the observed reflectance and affecting remote sensing detections.To enhance the accuracy of maize growth monitoring in dust-affected regions,this study aims to quantify the effect of sand dust retention on maize during the tasseling stage in the Kashgar Prefecture,Xinjiang Uygur Autonomous Region,China,by analyzing changes in canopy reflectance and vegetation indices.First,field sampling was conducted to measure the key canopy structure parameters and dust retention levels of maize,and laboratory spectral measurements were performed on leaf spectral properties under gradient dust retention.The measured data were then used to drive the LargE-Scale remote sensing data and image Simulation framework(LESS)model for simulating realistic maize canopy spectra across different dust levels,with validation against Sentinel-2 imagery.Second,on the basis of the simulated and satellite-derived spectra,the dust resistance of 36 common vegetation indices was systematically evaluated,and new robust dust-resistant indices were developed.The results showed that compared with dust-free maize,the canopy reflectance of dust-retained maize followed an increase–decrease–increase pattern,with critical turning points at 735 and 1325 nm.The maximum reflectance difference of–0.11755(change rate:29.002%)occurred within the 735–1325 nm range at 24 g/m^(2)dust retention,and the minimum reflectance difference of 0.04285(change rate:148.950%)was observed in the 350–735 nm range under the same dust retention level.Among the 36 vegetation indices,only the global environment monitoring index(GEMI)and the ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index(TCARI/OSAVI)exhibited dust resistance,with GEMI being effective below 6 g/m^(2)and TCARI/OSAVI remaining stable across all levels(average ratio:0.970).The newly developed indices in this study,(RE3–RE2)/(NIR–RE2),(RE3–RE2)/(RE4–RE2),and(NIR–RE2)/(RE4–RE2),retained values within the predefined dust-resistant range over the full dust retention levels of 0–24 g/m^(2),thus showing a more stable dust resistance compared with the commonly used 36 vegetation indices.Specially,(RE3–RE2)/(RE4–RE2)performed the most robustly in Sentinel-2 imagery,that is,58.020%of pixels were within the dust-resistant range,and an average ratio of 0.937 was obtained for the original-spectra index.This study provides a scientific basis for crop monitoring and management in dust-affected regions.
基金supported by the China Scholarship CouncilPostgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX17_0776)the Natural Science Foundation of NUPT(No.NY214039)
文摘A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.
基金National Natural Science Foundation of China(52435011,51821003,62175219,62103385)。
文摘Microwave chips are widely utilized in modern communication,national defense,and various technological domains.However,effective signal identification remains challenging due to complex multi-frequency microwave interference.To address this issue,we propose an advanced optical imaging framework based on nitrogen-vacancy(NV)center near-field microscopy.This framework enables the separation and imaging characterization of mixed multi-frequency microwave signals across a wide field of view(2000μm×1600μm,spatial resolution of 5μm)on chip surfaces.By leveraging the NV color center as a mixer,combined with a multi-frequency hybrid model and fast Fourier transform(FFT)analysis,we convert the invisible electromagnetic waves into visible optical information.Using a wide-field microscopy system equipped with a high-speed optical camera,our approach effectively enables the separation and imaging of mixed microwave signals across two complex scenarios.Comparative analysis with finite element simulation validates the accuracy of this approach.Experimental results reveal m Hz frequency resolution for GHz microwaves andμT-level signal intensity resolution,showcasing its superior capability for imaging mixed signals with multi-frequency.These findings provide critical technical support for microwave chip characterization,interference signal identification,and diagnostic testing,highlighting the broad applicability of this technique.
基金supported by the National Natural Science Foundation of China(Grant Nos.62366014,61961019)the Jiangxi Provincial Natural Science Foundation(Grant No.20232BAB202008)。
文摘This study introduces an effective framework for image encryption,leveraging the principles of chaos theory through the use of cellular automata neighborhood(CAN)and a novel two-dimensional hyperchaotic map(2D-SGHM)derived from the classic sine and Gauss maps.The core of our investigation delved into the basic performance and dynamical behaviors of this map.The findings reveal a wide hyperchaotic range characterized by large positive Lyapunov exponents,establishing map superiority in image encryption.By integrating different cellular automata neighborhoods,we can generate diverse image encryption schemes.Specifically,this study highlights three distinct image encryption algorithms constructed from one-dimensional,von Neumann and Moore neighborhoods,named OIEA,NIEA,and MIEA.Each scheme is uniquely designed to harness the benefits of CAN for encryption,thereby enhancing the overall effectiveness and security level of the image encryption process.In the experiment,the number of pixels change rates for OIEA,NIEA,and MIEA are 99.6097%,99.6092%,and 99.6098%,and the unified average changing intensities are 33.4603%,33.4626%,and 33.4628%,respectively.The correlation coefficients of the neighboring pixels are notably low,recorded at 0.00065,0.00064,and 0.00031 for each scheme,while the information entropies are nearly identical,with scores of 7.9977,7.9975,and 7.9976,showing that our encryption schemes have high security performance and can provide reliable security for different types of data information.
基金funded by the National Key Research and Development Program of China under Grants 2020YFB2104400 and 2020YFB2104401the National Natural Science Foundation of China under Grant 82260362the Hainan Major Science and Technology Program of China under Grant ZDKJ202017.
文摘Remote sensing image(RSI)with concurrently high spatial,temporal,and spectral resolutions cannot be produced by a single sensor.Multisource RSI fusion is a convenient technique to realize high spatial resolution multispectral(MS)images(spatial spectral fusion,i.e.SSF)and high temporal and spatial resolution MS images(spatiotemporal fusion,i.e.STF).Currently,deep learning-based fusion models can only implement SSF or STF,lacking models that perform both SSF and STF.Multiresolution generative adversarial networks with bidirectional adaptive-stage progressive guided fusion(BAPGF)for RSI are proposed to implement both SSF and STF,namely BPF-MGAN.A bidirectional adaptive-stage feature extraction architecture infine-scale-to-coarse-scale and coarse-scale-to-fine-scale modes is introduced.The designed BAPGF introduces a previous fusion result-oriented cross-stage-level dual-residual attention fusion strategy to enhance critical information and suppress superfluous information.Adaptive resolution U-shaped discriminators are implemented to feed multiresolution context into the generator.A generalized multitask loss function unlimited by no-reference images is developed to strengthen the model via constraints on the multiscale feature,structural,and content similarities.The BPF-MGAN model is validated on SSF datasets and STF datasets.Compared with the state-of-the-art SSF and STF models,results demonstrate the superior performance of the proposed BPF-MGAN model in both subjective and objective evaluations.
