Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential....Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.展开更多
We discuss remote-sensing-image fusion based on a multi-band wavelet and RGB feature fusion method. The fused data can be used to monitor the dynamic evolution of mining induced subsidence. High resolution panchromati...We discuss remote-sensing-image fusion based on a multi-band wavelet and RGB feature fusion method. The fused data can be used to monitor the dynamic evolution of mining induced subsidence. High resolution panchromatic image data and multi-spectral image data were first decomposed with a multi-ary wavelet method. Then the high frequency components of the high resolution image were fused with the features from the R, G, B bands of the multi-spectral image to form a new high frequency component. Then the newly formed high frequency component and the low frequency component were inversely transformed using a multi-ary wavelet method. Finally, color images were formed from the newly formed R, G, B bands. In our experiment we used images with a resolution of 10 m (SPOT), and TM30 images, of the Huainan mining area. These images were fused with a trinary wavelet method. In addition, we used four indexes—entropy, average gradient, wavelet energy and spectral distortion—to assess the new method. The result indicates that this new method can improve the clarity and resolution of the images and also preserves the information from the original images. Using the fused images for monitoring mining induced subsidence achieves a good effect.展开更多
In general, there is a demand for real-time processing of mass quantity remote sensing images. However, the task is not only data-intensive but also computating-intensive. Distributed processing is a hot topic in remo...In general, there is a demand for real-time processing of mass quantity remote sensing images. However, the task is not only data-intensive but also computating-intensive. Distributed processing is a hot topic in remote sensing processing and image deblurring is also one of the most important needs. In order to satisfy the demand for quick proc- essing and deblurring of mass quantity satellite images, we developed a distributed, grid computation-based platform as well as a corresponding middleware for grid computation. Both a constrained power spectrum equalization algorithm and effective block processing measures, which can avoid boundary effect, were applied during the processing. The re- sult is satisfactory since computation efficiency and visual effect were greatly improved. It can be concluded that the technology of spatial information grids is effective for mass quantity remote sensing image processing.展开更多
Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,ru...Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.展开更多
Based on the satellite remote sensing TM/ETM images of Xuzhou city, basic data about land use of the city from 1994 to 2000 are obtained with the neural network classification module of PCI software, and the dynamic c...Based on the satellite remote sensing TM/ETM images of Xuzhou city, basic data about land use of the city from 1994 to 2000 are obtained with the neural network classification module of PCI software, and the dynamic con- version matrix of land use is thus calculated. The areas of construction land and water body have increased by 1833.93 hm2 and 804.87 hm2, respectively. On the contrary, the area of cropland has decreased by 3207.24 hm2. The area of cropland converted into construction land makes up 26.84%, and that converted into water body amounts for 8.17% of the total area of cropland in 1994. The variation index of land use degree and the dynamic degree index of land use computed are 1.38 and 57.81%, respectively, which demonstrate that land use in Xuzhou is in a development period and the changes are drastic. The frequency index and importance index of the form in which cropland converted into con- struction land are 29.91% and 68.93% respectively. The results indicate that the change is not only widespread in space but a major form of spatial change of land use in the area.展开更多
In this paper, an atmospheric correction method to TM image is presented, which can simulate the atmospheric correction parameters, such as optical depth, sky radiance and path radiance at the time the satellite passe...In this paper, an atmospheric correction method to TM image is presented, which can simulate the atmospheric correction parameters, such as optical depth, sky radiance and path radiance at the time the satellite passes,by using interpolation among local meteorological records, parameterization models and dark pixels. The TM image of the Nanjing area in China was corrected by this method. For analyzing the accuracy of this method, the calculated reflectance, apparent reflectance and ground measured reflectance were compared. NDVI before and after atmospheric cor- rection were also compared. The results show that the method is applicable and efficient in the visible to near infrared band of TM image. In order to improve the accuracy of the method, the infrared spectrum measured data for the two other bands of TM image are required in future field investigations. The method is suitable to many other satellite optical remote sensing images with the same or similar spectral characteristics of TM images.展开更多
A novel fusion method of multispectral image and panchromatic image based on nonsubsampled contourlet transform（NSCT） and non-negative matrix factorization（NMF） is presented,the aim of which is to preserve both sp...A novel fusion method of multispectral image and panchromatic image based on nonsubsampled contourlet transform（NSCT） and non-negative matrix factorization（NMF） is presented,the aim of which is to preserve both spectral and spatial information simultaneously in fused image.NMF is a matrix factorization method,which can extract the local feature by choosing suitable dimension of the feature subspace.Firstly the multispectral image was represented in intensity hue saturation（IHS） system.Then the I component and panchromatic image were decomposed by NSCT.Next we used NMF to learn the feature of both multispectral and panchromatic images＇ low-frequency subbands,and the selection principle of the other coefficients was absolute maximum criterion.Finally the new coefficients were reconstructed to get the fused image.Experiments are carried out and the results are compared with some other methods,which show that the new method performs better in improving the spatial resolution and preserving the feature information than the other existing relative methods.展开更多
Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial i...Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial infor- mation is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.展开更多
A novel reference-driven method for MR image reconstruction based on wavelet sparsity and nonlocal total variation（NLTV）is proposed.Utilizing the sparsity of the difference image between the target image and the mot...A novel reference-driven method for MR image reconstruction based on wavelet sparsity and nonlocal total variation（NLTV）is proposed.Utilizing the sparsity of the difference image between the target image and the motion-compensated reference image in wavelet transform domain,the proposed method does not need to estimate contrast changes and therefore increases computational efficiency.Additionally,NLTV regularization is applied to preserve image details and features without blocky effects.An efficient alternating iterative algorithm is used to estimate motion effects and reconstruct the difference image.Experimental results demonstrate that the proposed method can significantly reduce sampling rate or improve the quality of the reconstructed image alternatively.展开更多
This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, o...This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, often based on the encoder-decoder structure and utilizing multiple upsampling and downsampling layers, are computationally expensive. To improve efficiency, the paper proposes two modules: the efficient spatial resolution recovery module(ESRR) for upsampling and the efficient depth information augmentation module(EDIA) for downsampling.These modules not only reduce model complexity but also enhance performance. Additionally, the partial feature weight learning module(PFWL) is introduced to reduce the computational burden by applying weight learning across partial dimensions, rather than using full-channel convolution.To overcome the limitations of convolutional neural networks(CNN)-based networks, the haze distribution index transformer(HDIT) is integrated into the decoder. We also propose the physicalbased non-adjacent feature fusion module(PNFF), which leverages the atmospheric scattering model to improve generalization of our MDWR-Net. The MDWR-Net achieves superior dehazing performance with a computational cost of just 2.98×10^(9) multiply-accumulate operations(MACs),which is less than one-tenth of previous methods. Experimental results validate its effectiveness in balancing performance and computational efficiency.展开更多
In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have differ...In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.展开更多
Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China.This destructive disease has the characteristics of bring wide-spread,fast ons...Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China.This destructive disease has the characteristics of bring wide-spread,fast onset,and long incubation time.Most importantly,in China,the fatality rate in pines is as high as 100%.The key to reducing this mortality is how to quickly find the infected trees.We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool.This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite.The recognition accuracy of the test data set was 99.4%,and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees.It can provide strong technical support for the prevention and control of pine wilt disease.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.91948303)。
文摘Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.
基金Project 2003-38 supported by the Geological Investigation Item of Anhui Province
文摘We discuss remote-sensing-image fusion based on a multi-band wavelet and RGB feature fusion method. The fused data can be used to monitor the dynamic evolution of mining induced subsidence. High resolution panchromatic image data and multi-spectral image data were first decomposed with a multi-ary wavelet method. Then the high frequency components of the high resolution image were fused with the features from the R, G, B bands of the multi-spectral image to form a new high frequency component. Then the newly formed high frequency component and the low frequency component were inversely transformed using a multi-ary wavelet method. Finally, color images were formed from the newly formed R, G, B bands. In our experiment we used images with a resolution of 10 m (SPOT), and TM30 images, of the Huainan mining area. These images were fused with a trinary wavelet method. In addition, we used four indexes—entropy, average gradient, wavelet energy and spectral distortion—to assess the new method. The result indicates that this new method can improve the clarity and resolution of the images and also preserves the information from the original images. Using the fused images for monitoring mining induced subsidence achieves a good effect.
基金Project 2003AA135010 supported by the National High Technology Research and Development Program of China
文摘In general, there is a demand for real-time processing of mass quantity remote sensing images. However, the task is not only data-intensive but also computating-intensive. Distributed processing is a hot topic in remote sensing processing and image deblurring is also one of the most important needs. In order to satisfy the demand for quick proc- essing and deblurring of mass quantity satellite images, we developed a distributed, grid computation-based platform as well as a corresponding middleware for grid computation. Both a constrained power spectrum equalization algorithm and effective block processing measures, which can avoid boundary effect, were applied during the processing. The re- sult is satisfactory since computation efficiency and visual effect were greatly improved. It can be concluded that the technology of spatial information grids is effective for mass quantity remote sensing image processing.
基金supported by the State Grid Science&Technology Project of China(5400-202224153A-1-1-ZN).
文摘Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.
基金Projects 40401038 supported by National Natural Science Foundation of China, and 05KJB420133 by Natural Science Foundation for Colleges and Universities in Jiangsu Province
文摘Based on the satellite remote sensing TM/ETM images of Xuzhou city, basic data about land use of the city from 1994 to 2000 are obtained with the neural network classification module of PCI software, and the dynamic con- version matrix of land use is thus calculated. The areas of construction land and water body have increased by 1833.93 hm2 and 804.87 hm2, respectively. On the contrary, the area of cropland has decreased by 3207.24 hm2. The area of cropland converted into construction land makes up 26.84%, and that converted into water body amounts for 8.17% of the total area of cropland in 1994. The variation index of land use degree and the dynamic degree index of land use computed are 1.38 and 57.81%, respectively, which demonstrate that land use in Xuzhou is in a development period and the changes are drastic. The frequency index and importance index of the form in which cropland converted into con- struction land are 29.91% and 68.93% respectively. The results indicate that the change is not only widespread in space but a major form of spatial change of land use in the area.
基金Project 2003DKA1T007 supported by the National Facility Information Infrastructure (China-NFII) Foundation of the Ministry of Science and Technology
文摘In this paper, an atmospheric correction method to TM image is presented, which can simulate the atmospheric correction parameters, such as optical depth, sky radiance and path radiance at the time the satellite passes,by using interpolation among local meteorological records, parameterization models and dark pixels. The TM image of the Nanjing area in China was corrected by this method. For analyzing the accuracy of this method, the calculated reflectance, apparent reflectance and ground measured reflectance were compared. NDVI before and after atmospheric cor- rection were also compared. The results show that the method is applicable and efficient in the visible to near infrared band of TM image. In order to improve the accuracy of the method, the infrared spectrum measured data for the two other bands of TM image are required in future field investigations. The method is suitable to many other satellite optical remote sensing images with the same or similar spectral characteristics of TM images.
基金Supported by the National Natural Science Foundation of China(60872065)
文摘A novel fusion method of multispectral image and panchromatic image based on nonsubsampled contourlet transform（NSCT） and non-negative matrix factorization（NMF） is presented,the aim of which is to preserve both spectral and spatial information simultaneously in fused image.NMF is a matrix factorization method,which can extract the local feature by choosing suitable dimension of the feature subspace.Firstly the multispectral image was represented in intensity hue saturation（IHS） system.Then the I component and panchromatic image were decomposed by NSCT.Next we used NMF to learn the feature of both multispectral and panchromatic images＇ low-frequency subbands,and the selection principle of the other coefficients was absolute maximum criterion.Finally the new coefficients were reconstructed to get the fused image.Experiments are carried out and the results are compared with some other methods,which show that the new method performs better in improving the spatial resolution and preserving the feature information than the other existing relative methods.
基金Supported by the National Major Scientific Instruments Development Project of China under Grant No 2013YQ030595the National Natural Science Foundation of China under Grant Nos 11675014,61601442,61605218,61474123 and 61575207+2 种基金the Science and Technology Innovation Foundation of Chinese Academy of Sciences under Grant No CXJJ-16S047,the National Defense Science and Technology Innovation Foundation of Chinese Academy of Sciencesthe Program of International S&T Cooperation under Grant No 2016YFE0131500the Advance Research Project under Grant No 30102070101
文摘Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial infor- mation is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.
基金Supported by the National Natural Science Foundation of China(61077022)
文摘A novel reference-driven method for MR image reconstruction based on wavelet sparsity and nonlocal total variation（NLTV）is proposed.Utilizing the sparsity of the difference image between the target image and the motion-compensated reference image in wavelet transform domain,the proposed method does not need to estimate contrast changes and therefore increases computational efficiency.Additionally,NLTV regularization is applied to preserve image details and features without blocky effects.An efficient alternating iterative algorithm is used to estimate motion effects and reconstruct the difference image.Experimental results demonstrate that the proposed method can significantly reduce sampling rate or improve the quality of the reconstructed image alternatively.
文摘This paper introduces a lightweight remote sensing image dehazing network called multidimensional weight regulation network(MDWR-Net), which addresses the high computational cost of existing methods. Previous works, often based on the encoder-decoder structure and utilizing multiple upsampling and downsampling layers, are computationally expensive. To improve efficiency, the paper proposes two modules: the efficient spatial resolution recovery module(ESRR) for upsampling and the efficient depth information augmentation module(EDIA) for downsampling.These modules not only reduce model complexity but also enhance performance. Additionally, the partial feature weight learning module(PFWL) is introduced to reduce the computational burden by applying weight learning across partial dimensions, rather than using full-channel convolution.To overcome the limitations of convolutional neural networks(CNN)-based networks, the haze distribution index transformer(HDIT) is integrated into the decoder. We also propose the physicalbased non-adjacent feature fusion module(PNFF), which leverages the atmospheric scattering model to improve generalization of our MDWR-Net. The MDWR-Net achieves superior dehazing performance with a computational cost of just 2.98×10^(9) multiply-accumulate operations(MACs),which is less than one-tenth of previous methods. Experimental results validate its effectiveness in balancing performance and computational efficiency.
文摘In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.
基金supported by the National Science and Technology Major Project of China’s High Resolution Earth Observation System(21-Y30B02-9001-19/22)the Heilongjiang Provincial Natural Science Foundation of China(YQ2020C018)。
文摘Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China.This destructive disease has the characteristics of bring wide-spread,fast onset,and long incubation time.Most importantly,in China,the fatality rate in pines is as high as 100%.The key to reducing this mortality is how to quickly find the infected trees.We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool.This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite.The recognition accuracy of the test data set was 99.4%,and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees.It can provide strong technical support for the prevention and control of pine wilt disease.
