Pub Date : 2024-11-12DOI: 10.1109/LGRS.2024.3496567
Ignacio Masari;Gabriele Moser;Sebastiano B. Serpico
Unsupervised change detection (CD) stands as a critical tool for damage assessment after a natural disaster. We emphasize heterogeneous CD methods, which support the case of highly heterogeneous images at the two observation dates, providing greater flexibility than traditional homogeneous methods. This adaptability is vital for swift responses in the aftermath of natural disasters. In this framework, we address the challenging case of detecting changes between the hyperspectral and synthetic aperture radar images. This case has intrinsic difficulties, namely, the difference in the nature of the physical quantity measured, added to the great difference in dimensionality of the two imaging domains. To address these challenges, a novel method is proposed based on the integration of a manifold learning technique and deep learning networks trained to perform an image-to-image translation task. The method works in a fully unsupervised manner, further enforcing a fast implementation in real-world scenarios. From an application-oriented perspective, we focus on flooded-area mapping using the PRISMA and COSMO-SkyMed missions. The experimental validation on two datasets, a semisimulated one and a real one associated with flooding, suggests that the proposed method allows for accurate detection of flooded areas and other ground changes.
{"title":"Manifold Learning and Deep Generative Networks for Heterogeneous Change Detection From Hyperspectral and Synthetic Aperture Radar Images","authors":"Ignacio Masari;Gabriele Moser;Sebastiano B. Serpico","doi":"10.1109/LGRS.2024.3496567","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3496567","url":null,"abstract":"Unsupervised change detection (CD) stands as a critical tool for damage assessment after a natural disaster. We emphasize heterogeneous CD methods, which support the case of highly heterogeneous images at the two observation dates, providing greater flexibility than traditional homogeneous methods. This adaptability is vital for swift responses in the aftermath of natural disasters. In this framework, we address the challenging case of detecting changes between the hyperspectral and synthetic aperture radar images. This case has intrinsic difficulties, namely, the difference in the nature of the physical quantity measured, added to the great difference in dimensionality of the two imaging domains. To address these challenges, a novel method is proposed based on the integration of a manifold learning technique and deep learning networks trained to perform an image-to-image translation task. The method works in a fully unsupervised manner, further enforcing a fast implementation in real-world scenarios. From an application-oriented perspective, we focus on flooded-area mapping using the PRISMA and COSMO-SkyMed missions. The experimental validation on two datasets, a semisimulated one and a real one associated with flooding, suggests that the proposed method allows for accurate detection of flooded areas and other ground changes.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1109/LGRS.2024.3496730
Hongche Yin;Pengwei Zhou;Guozheng Xu;Gaoming He;Li Li;Jian Yao
The global optimization-based color correction approach aims to minimize the color differences of multiple images by optimizing the correction model for each image. The color differences in multisource and multitemporal remote sensing images are difficult to express using a simple correction model with few parameters. When employing a more flexible correction model, the number of correction parameters and optimization equations grows rapidly with the increase in the number and resolution of input images. In addition, the correction parameters of all images are coupled together and need to be solved simultaneously. An excessive number of parameters results in solving slowly or potential failure. To solve this problem, we propose a parallelizable color correction approach that decouples the correlation of correction parameters in the optimization equations and optimizes each image separately. First, we introduce auxiliary variables that replace values related to other images in the cost function. Second, we construct optimization equations for each image and parallelly solve the correction parameters. Finally, we correct the input images through a weighted correction model to better eliminate correction artifacts. Our approach iteratively optimizes auxiliary variables and correction parameters until the correction results converge. The experimental results on several challenging datasets show that our approach significantly improves execution efficiency and obtains the global optimal solution using the flexible correction model.
{"title":"A Parallelizable Global Color Consistency Optimization Algorithm for Multiple Images","authors":"Hongche Yin;Pengwei Zhou;Guozheng Xu;Gaoming He;Li Li;Jian Yao","doi":"10.1109/LGRS.2024.3496730","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3496730","url":null,"abstract":"The global optimization-based color correction approach aims to minimize the color differences of multiple images by optimizing the correction model for each image. The color differences in multisource and multitemporal remote sensing images are difficult to express using a simple correction model with few parameters. When employing a more flexible correction model, the number of correction parameters and optimization equations grows rapidly with the increase in the number and resolution of input images. In addition, the correction parameters of all images are coupled together and need to be solved simultaneously. An excessive number of parameters results in solving slowly or potential failure. To solve this problem, we propose a parallelizable color correction approach that decouples the correlation of correction parameters in the optimization equations and optimizes each image separately. First, we introduce auxiliary variables that replace values related to other images in the cost function. Second, we construct optimization equations for each image and parallelly solve the correction parameters. Finally, we correct the input images through a weighted correction model to better eliminate correction artifacts. Our approach iteratively optimizes auxiliary variables and correction parameters until the correction results converge. The experimental results on several challenging datasets show that our approach significantly improves execution efficiency and obtains the global optimal solution using the flexible correction model.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-12DOI: 10.1109/LGRS.2024.3496753
Nermine Hendy;Akram Al-Hourani;Thomas Kraus;Maximilian Schandri;Markus Bachmann;Haytham M. Fayek
Radio frequency interference (RFI) in synthetic aperture radar (SAR) is a daunting challenge, affecting both sensing reliability and image quality. To ensure that SAR remains a powerful tool for Earth observation, this letter presents a 2-D variable attenuation space (azimuth)-frequency filtration (VASFF) method. This framework leverages the time-frequency characteristics of Level-0 SAR data, the RFI power profile, estimated RFI signal parameters, and the SAR antenna pattern to design a novel variable filter. Signal power localization estimates the interference source’s relative position, facilitating filter application. Simulated results, obtained using our open-source emulator, SEMUS, to generate both clean and interference-contaminated raw SAR data, demonstrate that the proposed filter achieves a 2 dB improvement over traditional notch filtering. The framework is further tested on real-life interference events on TerraSAR-X revealing previously obscured image details, validating the framework’s effectiveness.
{"title":"Narrow-Band RFI Mitigation in Synthetic Aperture Radars Using Variable Space-Frequency Filter","authors":"Nermine Hendy;Akram Al-Hourani;Thomas Kraus;Maximilian Schandri;Markus Bachmann;Haytham M. Fayek","doi":"10.1109/LGRS.2024.3496753","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3496753","url":null,"abstract":"Radio frequency interference (RFI) in synthetic aperture radar (SAR) is a daunting challenge, affecting both sensing reliability and image quality. To ensure that SAR remains a powerful tool for Earth observation, this letter presents a 2-D variable attenuation space (azimuth)-frequency filtration (VASFF) method. This framework leverages the time-frequency characteristics of Level-0 SAR data, the RFI power profile, estimated RFI signal parameters, and the SAR antenna pattern to design a novel variable filter. Signal power localization estimates the interference source’s relative position, facilitating filter application. Simulated results, obtained using our open-source emulator, SEMUS, to generate both clean and interference-contaminated raw SAR data, demonstrate that the proposed filter achieves a 2 dB improvement over traditional notch filtering. The framework is further tested on real-life interference events on TerraSAR-X revealing previously obscured image details, validating the framework’s effectiveness.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1109/LGRS.2024.3495716
Kailiang Lu;Jianhua Yue;Jianmei Zhou;Ya’Nan Fan;Kerui Fan;He Li;Xiu Li
For large-scale geophysical models, the order of the coefficient matrix in 3-D transient electromagnetics (TEMs) forward modeling can reach millions or even tens of millions. Balancing computational efficiency and memory usage presents a challenge worthy of in-depth exploration. In this letter, we utilize an integral representation of the iterative error in the Arnoldi method to construct an efficient quadrature-based restarted forward algorithm. First, the mimetic finite volume (MFV) method on a staggered hexahedral grid is employed to discretize the time-domain Maxwell’s equations, expressing the TEM response after the step-off waveform shutoff as the product of the matrix exponential function $f({text {A}})$ and vector b. Then, using Cauchy’s integral formula, the expression of ${f}({text {A}}){b}$ is transformed into an integral form and approximated using the restarted Arnoldi (RA) algorithm. Our method does not require solving linear systems and can leverage GPU parallel technology and optimize the RA algorithm parameters to enhance computational efficiency. Comparative studies with other numerical methods validate the advantages and accuracy of our approach, which numerical example demonstrates can fully achieve large-scale fast 3-D TEM forward modeling.
{"title":"Quadrature-Based Restarted Arnoldi Method for Fast 3-D TEM Forward Modeling of Large-Scale Models","authors":"Kailiang Lu;Jianhua Yue;Jianmei Zhou;Ya’Nan Fan;Kerui Fan;He Li;Xiu Li","doi":"10.1109/LGRS.2024.3495716","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3495716","url":null,"abstract":"For large-scale geophysical models, the order of the coefficient matrix in 3-D transient electromagnetics (TEMs) forward modeling can reach millions or even tens of millions. Balancing computational efficiency and memory usage presents a challenge worthy of in-depth exploration. In this letter, we utilize an integral representation of the iterative error in the Arnoldi method to construct an efficient quadrature-based restarted forward algorithm. First, the mimetic finite volume (MFV) method on a staggered hexahedral grid is employed to discretize the time-domain Maxwell’s equations, expressing the TEM response after the step-off waveform shutoff as the product of the matrix exponential function \u0000<inline-formula> <tex-math>$f({text {A}})$ </tex-math></inline-formula>\u0000 and vector b. Then, using Cauchy’s integral formula, the expression of \u0000<inline-formula> <tex-math>${f}({text {A}}){b}$ </tex-math></inline-formula>\u0000 is transformed into an integral form and approximated using the restarted Arnoldi (RA) algorithm. Our method does not require solving linear systems and can leverage GPU parallel technology and optimize the RA algorithm parameters to enhance computational efficiency. Comparative studies with other numerical methods validate the advantages and accuracy of our approach, which numerical example demonstrates can fully achieve large-scale fast 3-D TEM forward modeling.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For deep reservoir exploration, precise inversion of the deep target is important. However, the resolution of full waveform inversion (FWI) in the deeper region may not be as fine as in the shallow region due to the acquisition geometry and complex local structure. Besides, the cycle-skipping problem is critical and significantly influences the accuracy of the inversion in FWI. In order to increase the resolution for the deep region and reduce the cycle-skipping problem, we propose a local angle domain-based inversion method. We decompose the incident and scattered wavefields around a local target into the local angle domain. Then, by the simultaneous construction of the local angle filter and the local resolution function based on the wavefield decomposition, a local angle domain multiscale inversion method with illumination compensation is conducted. Based on the convergence criterion, we construct an auto-transitional misfit function that can avoid manual intervention for the multiscale inversion process. Numerical tests proved the feasibility of the proposed strategy.
{"title":"Auto-Transitional Local Angle Domain Illumination Compensated Multiscale Full Waveform Inversion","authors":"Qiang Ma;Jingrui Luo;Huamin Zhou;Zhimin Yan;Lu Wang;Xingguo Huang","doi":"10.1109/LGRS.2024.3495817","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3495817","url":null,"abstract":"For deep reservoir exploration, precise inversion of the deep target is important. However, the resolution of full waveform inversion (FWI) in the deeper region may not be as fine as in the shallow region due to the acquisition geometry and complex local structure. Besides, the cycle-skipping problem is critical and significantly influences the accuracy of the inversion in FWI. In order to increase the resolution for the deep region and reduce the cycle-skipping problem, we propose a local angle domain-based inversion method. We decompose the incident and scattered wavefields around a local target into the local angle domain. Then, by the simultaneous construction of the local angle filter and the local resolution function based on the wavefield decomposition, a local angle domain multiscale inversion method with illumination compensation is conducted. Based on the convergence criterion, we construct an auto-transitional misfit function that can avoid manual intervention for the multiscale inversion process. Numerical tests proved the feasibility of the proposed strategy.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1109/LGRS.2024.3495659
Tie Zhong;Kaiyuan Zheng;Shiqi Dong;Xunqian Tong;Xintong Dong
The quality of seismic images is often affected by the limitation of acquisition conditions and the interference of noises, which causes the low resolution of seismic images and misleads the following geological interpretation. Although the super-resolution method for seismic images based on convolutional neural network (CNN) has behaved well, the quality of weak events especially deep events is still need to be improved, due to CNN is limited by the receptive fields, which results in weaker ability to perceive relationships among pixels far apart. In this letter, we solve this problem by designing a combination network of CNN and transformer (CNCT). CNCT consists of three parts, edge feature fusion block (EFB), deep feature mining block (DMB), and feature enhancement block (FEB). The EFB aims to fuse the input low-resolution (LR) image and the corresponding edges obtained by the Sobel algorithm and performs preliminary shallow feature extraction. DMB mines deeper features by stacking residual blocks, and each residual block makes full use of its excellent perception of global and local information by combining transformer and CNN. Finally, the FEB uses subpixel convolution for upsampling to expand the size of feature maps. The experimental results on synthetic data and field data show that CNCT not only behaves better on perception effect and texture details than that of other deep learning (DL) methods but also can suppress noise and improve the dominant frequency.
{"title":"Enhancing the Resolution of Seismic Images With a Network Combining CNN and Transformer","authors":"Tie Zhong;Kaiyuan Zheng;Shiqi Dong;Xunqian Tong;Xintong Dong","doi":"10.1109/LGRS.2024.3495659","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3495659","url":null,"abstract":"The quality of seismic images is often affected by the limitation of acquisition conditions and the interference of noises, which causes the low resolution of seismic images and misleads the following geological interpretation. Although the super-resolution method for seismic images based on convolutional neural network (CNN) has behaved well, the quality of weak events especially deep events is still need to be improved, due to CNN is limited by the receptive fields, which results in weaker ability to perceive relationships among pixels far apart. In this letter, we solve this problem by designing a combination network of CNN and transformer (CNCT). CNCT consists of three parts, edge feature fusion block (EFB), deep feature mining block (DMB), and feature enhancement block (FEB). The EFB aims to fuse the input low-resolution (LR) image and the corresponding edges obtained by the Sobel algorithm and performs preliminary shallow feature extraction. DMB mines deeper features by stacking residual blocks, and each residual block makes full use of its excellent perception of global and local information by combining transformer and CNN. Finally, the FEB uses subpixel convolution for upsampling to expand the size of feature maps. The experimental results on synthetic data and field data show that CNCT not only behaves better on perception effect and texture details than that of other deep learning (DL) methods but also can suppress noise and improve the dominant frequency.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1109/LGRS.2024.3494800
Huiying Zheng;Hao Liu;Jian Yang;Yue Ma;Xiao Hua Wang
The diffuse attenuation coefficient $K_{d}$ continues to play a crucial role in oceanographic research works. Recently, Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has shown its great ability to estimate $K_{d}$ using the water column decay profiles. However, the weak water column backscattered signals are vulnerable to afterpulses and solar background noise, making this way perform not well in the daytime and in nearshore areas. In this study, a method to estimate $K_{d}$ is proposed which innovatively uses ICESat-2 bathymetric signal intensities. The main principle is to calculate the attenuation in water column transmission by bathymetric lidar equations. Since the seafloor signal level is much stronger than that of the water column, a significant advantage is the greater noise immunity, i.e., the ability to operate under strong background noise and afterpulses interference. The performance is validated against the moderate-resolution imaging spectroradiometer (MODIS) ocean color measurements with mean relative differences (MRDs) of <32% using both daytime and nighttime ICESat-2 data in six sea and large lake nearshore areas. Based on the new generation of spaceborne lidar data, this study explores a new path to monitor water qualities in nearshore areas. This method is applicable where seafloor photons exist in both daytime and nighttime.
{"title":"Deriving Water Diffuse Attenuation Coefficient Kd Using ICESat-2 Bathymetric Information","authors":"Huiying Zheng;Hao Liu;Jian Yang;Yue Ma;Xiao Hua Wang","doi":"10.1109/LGRS.2024.3494800","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3494800","url":null,"abstract":"The diffuse attenuation coefficient \u0000<inline-formula> <tex-math>$K_{d}$ </tex-math></inline-formula>\u0000 continues to play a crucial role in oceanographic research works. Recently, Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has shown its great ability to estimate \u0000<inline-formula> <tex-math>$K_{d}$ </tex-math></inline-formula>\u0000 using the water column decay profiles. However, the weak water column backscattered signals are vulnerable to afterpulses and solar background noise, making this way perform not well in the daytime and in nearshore areas. In this study, a method to estimate \u0000<inline-formula> <tex-math>$K_{d}$ </tex-math></inline-formula>\u0000 is proposed which innovatively uses ICESat-2 bathymetric signal intensities. The main principle is to calculate the attenuation in water column transmission by bathymetric lidar equations. Since the seafloor signal level is much stronger than that of the water column, a significant advantage is the greater noise immunity, i.e., the ability to operate under strong background noise and afterpulses interference. The performance is validated against the moderate-resolution imaging spectroradiometer (MODIS) ocean color measurements with mean relative differences (MRDs) of <32% using both daytime and nighttime ICESat-2 data in six sea and large lake nearshore areas. Based on the new generation of spaceborne lidar data, this study explores a new path to monitor water qualities in nearshore areas. This method is applicable where seafloor photons exist in both daytime and nighttime.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1109/LGRS.2024.3495661
Jiachun Wang;Yun Lin;Deyun Ma;Yanping Wang;Shengbo Ye
The suppression of clutter and the positioning of underground pipelines are crucial steps in the processing of ground-penetrating radar (GPR) data. It is challenging to acquire clutter-free measured data during the radar detection process. As a result, the existing deep learning (DL) methods are primarily trained using simulation data, which limits their applicability to real-world scenarios. To address these challenges, this letter proposes an improved underground clutter suppression and pipeline positioning network. In the first stage, the model is trained using both measured data and simulation clutter-free data to enhance its ability to suppress clutter in measured data. Furthermore, in the second stage, the network is modified to accept paired, labeled simulation data, which enables more accurate pipeline positioning than the original unpaired network. Real-world data evidence demonstrates that the proposed network’s clutter suppression achieves a mean squared error (mse) of 0.006 and a peak signal-to-noise ratio (PSNR) of 34.73 dB. Additionally, the Euclidean distance error of the target clustering center coordinates is 0.82px. Compared to other methods, the performance of the proposed approach has been significantly enhanced.
{"title":"CycleGAN-Based Clutter Suppression and Pipeline Positioning Method for GPR Image","authors":"Jiachun Wang;Yun Lin;Deyun Ma;Yanping Wang;Shengbo Ye","doi":"10.1109/LGRS.2024.3495661","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3495661","url":null,"abstract":"The suppression of clutter and the positioning of underground pipelines are crucial steps in the processing of ground-penetrating radar (GPR) data. It is challenging to acquire clutter-free measured data during the radar detection process. As a result, the existing deep learning (DL) methods are primarily trained using simulation data, which limits their applicability to real-world scenarios. To address these challenges, this letter proposes an improved underground clutter suppression and pipeline positioning network. In the first stage, the model is trained using both measured data and simulation clutter-free data to enhance its ability to suppress clutter in measured data. Furthermore, in the second stage, the network is modified to accept paired, labeled simulation data, which enables more accurate pipeline positioning than the original unpaired network. Real-world data evidence demonstrates that the proposed network’s clutter suppression achieves a mean squared error (mse) of 0.006 and a peak signal-to-noise ratio (PSNR) of 34.73 dB. Additionally, the Euclidean distance error of the target clustering center coordinates is 0.82px. Compared to other methods, the performance of the proposed approach has been significantly enhanced.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The sparse subband fusion techniques achieve range super-resolution and show potential for application in super-resolution detection of multiple moving targets. This application requires intersubband range and Doppler alignments for multiple moving targets. However, existing algorithms often rely on strict assumptions about target velocities, which significantly limits their applicability. To address this issue, this letter introduces a velocity-local-compensation improved Keystone transform (VLC-IKT) for intrasubband motion compensation and intersubband Doppler alignment, without imposing constraints on target velocities. Additionally, an improved range profiles cross-correlation algorithm (IRPCC) is proposed to align intersubband ranges. The simulation results confirm that the proposed algorithm effectively aligns intersubband range and Doppler for multiple moving targets with arbitrary velocities, significantly enhancing both the fused signal-to-noise ratio (SNR) and probability of detection (POD), especially in low SNR conditions, thereby establishing a foundation for applying sparse subband fusion to super-resolution detection of multiple moving targets.
{"title":"A Range and Doppler Alignment Algorithm for Multiple Moving Targets in Sparse Subband Fusion","authors":"Yiheng Liu;Hua Zhang;Xuemei Wang;Qinghai Dong;Xiaode Lyu","doi":"10.1109/LGRS.2024.3495675","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3495675","url":null,"abstract":"The sparse subband fusion techniques achieve range super-resolution and show potential for application in super-resolution detection of multiple moving targets. This application requires intersubband range and Doppler alignments for multiple moving targets. However, existing algorithms often rely on strict assumptions about target velocities, which significantly limits their applicability. To address this issue, this letter introduces a velocity-local-compensation improved Keystone transform (VLC-IKT) for intrasubband motion compensation and intersubband Doppler alignment, without imposing constraints on target velocities. Additionally, an improved range profiles cross-correlation algorithm (IRPCC) is proposed to align intersubband ranges. The simulation results confirm that the proposed algorithm effectively aligns intersubband range and Doppler for multiple moving targets with arbitrary velocities, significantly enhancing both the fused signal-to-noise ratio (SNR) and probability of detection (POD), especially in low SNR conditions, thereby establishing a foundation for applying sparse subband fusion to super-resolution detection of multiple moving targets.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1109/LGRS.2024.3495976
Fengda Zhao;Zihan Zhou;Haobing Zhai;Pengwei Zhang;Xianshan Li
Lithology identification is crucial for oil and gas exploration and reservoir evaluation, involving the analysis of physical and chemical characteristics of geological samples through well-logging data. This process requires understanding the complex nonlinear relationships between logging parameters and lithology. Recently, graph neural networks have gained prominence for their ability to uncover hidden relationships among samples, enhancing lithology identification. However, the imbalanced distribution of logging data often leads to incorrect interclass connections in logging graphs, which can skew feature aggregation and reduce prediction accuracy. To address this issue, this letter introduces the residual graph attention network (ResGAT), which integrates the residual information of well-logging data into the graph network based on graph relationships, adds residual connections to mitigate the impact of interclass edges, and enhances the weight of original information. To authentically assess the model’s practical effectiveness, we, respectively, conducted cross-well predictions in completely isolated well sets in oil fields in Daqing, China, and Kansas, USA. Compared to conventional GAT and GCN models, our proposed method achieves higher identification accuracy and significantly improves prediction accuracy for minority classes.
岩性识别对于油气勘探和储层评价至关重要，涉及通过测井数据分析地质样本的物理和化学特征。这一过程需要了解测井参数与岩性之间复杂的非线性关系。最近，图神经网络因其揭示样本间隐藏关系的能力而备受瞩目，从而提高了岩性识别能力。然而，测井数据的不平衡分布往往会导致测井图中不正确的类间连接，从而使特征聚合出现偏差，降低预测精度。为解决这一问题，本文介绍了残差图注意网络（ResGAT），它根据图关系将测井数据的残差信息整合到图网络中，添加残差连接以减轻类间边缘的影响，并增强原始信息的权重。为了真实评估该模型的实际效果，我们分别在中国大庆油田和美国堪萨斯油田的完全孤立井组中进行了跨井预测。与传统的 GAT 和 GCN 模型相比，我们提出的方法实现了更高的识别准确率，并显著提高了对少数类别的预测准确率。
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