The experimental detection of the changes in microwave radiation of rocks under pressure is key to identifying earthquake anomalies through satellite passive microwave remote sensing. However, such changes have not been comprehensively characterized due to the considerable differences in crustal lithology, weak microwave radiation signals, and strong environmental noise. Considering the intrinsic and significant diversity of different polarized microwave radiations of any materials, this study investigated the responses of different polarized microwave radiations during loading the rock materials. Thus, a synchronized detection system including multiple sensors was constructed at outdoor to reveal the stress-induced changes in C-band microwave brightness temperature (MBT) of diorite specimen. Experimental results show that both the horizontal and vertical MBT varied regularly with the changes of pressure; however, the trends of changes of MBT were greatly influenced by the polarization modes. Specifically, a positive correlation was illustrated between the change in vertical polarization MBT and cyclically varied pressure, during which the MBT changed with a rate of 0.033 K/MPa about. In contrast, the changes in horizontal polarization MBT exhibited a negative correlation with the varied pressure, and the MBT change rate was approximately −0.031 K/MPa. Based on the radiative transfer theory, it was found that the opposite MBT changes with respect to h- and v-polarizations are supposed to be caused by the dielectric anisotropy under uniaxial compression conditions. This study illustrates the significant and discernible MBT changes of diorite induced by the stress, which is helpful to identify the detectable microwave radiation anomalies before large earthquake occurrence.
对受压岩石微波辐射变化的实验探测是通过卫星无源微波遥感识别地震异常的关键。然而，由于地壳岩性差异大、微波辐射信号弱、环境噪声强等原因，这种变化尚未得到全面的表征。考虑到任何材料的不同极化微波辐射都具有内在的显著多样性，本研究调查了岩石材料加载过程中不同极化微波辐射的响应。因此，在室外构建了一个包括多个传感器的同步检测系统，以揭示应力引起的闪长岩试样 C 波段微波亮度温度（MBT）的变化。实验结果表明，水平和垂直方向的微波亮度温度随压力的变化而有规律地变化，但微波亮度温度的变化趋势在很大程度上受极化模式的影响。具体而言，垂直极化 MBT 的变化与周期性变化的压力之间呈正相关，其间 MBT 以 0.033 K/MPa 左右的速率变化。相反，水平极化 MBT 的变化与变化的压力呈负相关，MBT 的变化率约为 -0.031 K/MPa。根据辐射传递理论，研究发现在单轴压缩条件下，介电各向异性会导致 h 极化和 v 极化 MBT 发生相反的变化。这项研究说明了应力诱发的闪长岩的显著和可识别的 MBT 变化，有助于在大地震发生前识别可探测到的微波辐射异常。
{"title":"Dual-Polarization Responses of Microwave Radiation of Diorite in Process of Uniaxial Loading","authors":"Guangrui Dong;Wenfei Mao;Licheng Sun;Tao Zheng;Haofeng Dou;Lixin Wu","doi":"10.1109/LGRS.2024.3492325","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3492325","url":null,"abstract":"The experimental detection of the changes in microwave radiation of rocks under pressure is key to identifying earthquake anomalies through satellite passive microwave remote sensing. However, such changes have not been comprehensively characterized due to the considerable differences in crustal lithology, weak microwave radiation signals, and strong environmental noise. Considering the intrinsic and significant diversity of different polarized microwave radiations of any materials, this study investigated the responses of different polarized microwave radiations during loading the rock materials. Thus, a synchronized detection system including multiple sensors was constructed at outdoor to reveal the stress-induced changes in C-band microwave brightness temperature (MBT) of diorite specimen. Experimental results show that both the horizontal and vertical MBT varied regularly with the changes of pressure; however, the trends of changes of MBT were greatly influenced by the polarization modes. Specifically, a positive correlation was illustrated between the change in vertical polarization MBT and cyclically varied pressure, during which the MBT changed with a rate of 0.033 K/MPa about. In contrast, the changes in horizontal polarization MBT exhibited a negative correlation with the varied pressure, and the MBT change rate was approximately −0.031 K/MPa. Based on the radiative transfer theory, it was found that the opposite MBT changes with respect to h- and v-polarizations are supposed to be caused by the dielectric anisotropy under uniaxial compression conditions. This study illustrates the significant and discernible MBT changes of diorite induced by the stress, which is helpful to identify the detectable microwave radiation anomalies before large earthquake occurrence.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645518","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}
Knowledge distillation (KD) is an effective method for compressing models in object detection tasks. Due to limited computational capability, unmanned aerial vehicle-based object detection (UAV-OD) widely adopt the KD technique to obtain lightweight detectors. Existing methods often overlook the significant differences in feature space caused by the large gap in scale between the teacher and student models. This limitation hampers the efficiency of knowledge transfer during the distillation process. Furthermore, the complex backgrounds in aerial images make it challenging for the student model to efficiently learn the object features. In this letter, we propose a novel KD framework for UAV-OD. Specifically, a progressive distillation approach is designed to alleviate the feature gap between teacher and student models. Then, a new feature alignment method is provided to extract object-related features for enhancing the student model’s knowledge reception efficiency. Finally, extensive experiments are conducted to validate the effectiveness of our proposed approach. The results demonstrate that our proposed method achieves state-of-the-art performance on two datasets.
{"title":"Domain-Invariant Progressive Knowledge Distillation for UAV-Based Object Detection","authors":"Liang Yao;Fan Liu;Chuanyi Zhang;Zhiquan Ou;Ting Wu","doi":"10.1109/LGRS.2024.3492187","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3492187","url":null,"abstract":"Knowledge distillation (KD) is an effective method for compressing models in object detection tasks. Due to limited computational capability, unmanned aerial vehicle-based object detection (UAV-OD) widely adopt the KD technique to obtain lightweight detectors. Existing methods often overlook the significant differences in feature space caused by the large gap in scale between the teacher and student models. This limitation hampers the efficiency of knowledge transfer during the distillation process. Furthermore, the complex backgrounds in aerial images make it challenging for the student model to efficiently learn the object features. In this letter, we propose a novel KD framework for UAV-OD. Specifically, a progressive distillation approach is designed to alleviate the feature gap between teacher and student models. Then, a new feature alignment method is provided to extract object-related features for enhancing the student model’s knowledge reception efficiency. Finally, extensive experiments are conducted to validate the effectiveness of our proposed approach. The results demonstrate that our proposed method achieves state-of-the-art performance on two datasets.","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":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691665","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 : 2024-11-06DOI: 10.1109/LGRS.2024.3492208
Yichen Zhang;Zhi Gao;Wenbo Sun;Yao Lu;Yuhan Zhu
Neural radiance fields (NeRFs) have gained great success in 3-D representation and novel-view synthesis, which attracted great efforts devoted to this area. However, when rendering large-scale scenes from a drone perspective, existing NeRF methods exhibit pronounced distortions in scene detail including absent textures and blurring of small objects. In this letter, we propose MD-NeRF to mitigate such distortions by integrating a hybrid sampling strategy and an adaptive scene decomposition method. Specifically, an anti-aliasing sampling method combining spiral sampling and sampling along rays is presented to address rendering anomalies. In addition, we decompose a large scene into multiple subscenes using a mixture of expert (MoE) modules. A shared expert is introduced to capture common features and reduce redundancy across the specialized experts. Consequently, the combination of these two methods effectively minimizes distortions when rendering large-scale scenes and enables our model to produce finer textures and more coherent details. We have conducted extensive experiments on several large-scale unbounded scene datasets, and the results demonstrate that our approach has achieved state-of-the-art performance on all datasets, most notably evidenced by a 1-dB enhancement in PSNR metrics on the Mill19 dataset.
{"title":"MD-NeRF: Enhancing Large-Scale Scene Rendering and Synthesis With Hybrid Point Sampling and Adaptive Scene Decomposition","authors":"Yichen Zhang;Zhi Gao;Wenbo Sun;Yao Lu;Yuhan Zhu","doi":"10.1109/LGRS.2024.3492208","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3492208","url":null,"abstract":"Neural radiance fields (NeRFs) have gained great success in 3-D representation and novel-view synthesis, which attracted great efforts devoted to this area. However, when rendering large-scale scenes from a drone perspective, existing NeRF methods exhibit pronounced distortions in scene detail including absent textures and blurring of small objects. In this letter, we propose MD-NeRF to mitigate such distortions by integrating a hybrid sampling strategy and an adaptive scene decomposition method. Specifically, an anti-aliasing sampling method combining spiral sampling and sampling along rays is presented to address rendering anomalies. In addition, we decompose a large scene into multiple subscenes using a mixture of expert (MoE) modules. A shared expert is introduced to capture common features and reduce redundancy across the specialized experts. Consequently, the combination of these two methods effectively minimizes distortions when rendering large-scale scenes and enables our model to produce finer textures and more coherent details. We have conducted extensive experiments on several large-scale unbounded scene datasets, and the results demonstrate that our approach has achieved state-of-the-art performance on all datasets, most notably evidenced by a 1-dB enhancement in PSNR metrics on the Mill19 dataset.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636511","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 : 2024-11-06DOI: 10.1109/LGRS.2024.3492175
Hao Yi;Bo Liu;Bin Zhao;Enhai Liu
LiDAR-guided stereo matching for high-precision disparity estimation is a very promising task in photogrammetry and remote sensing. Unfortunately, existing methods suffer from the problem that it is difficult to automatically obtain appropriate stereo matching model parameters to ensure satisfactory results. To solve it, this letter proposes a LiDAR-guided stereo matching framework using Bayesian optimization with Gaussian process regression, which aims to automatically infer the stereo matching model parameters by LiDAR data. First, local matching model based on the belief propagation algorithm is designed. Second, the objective function is constructed by minimizing the difference between the local matching results and the LiDAR data. Third, Bayesian optimization with Gaussian process regression is applied to minimize this objective function to infer the model parameters. Finally, experimental results on the GaoFen-7 and UAV Stereo datasets show that the proposed method can effectively infer suitable model parameters from LiDAR data, and our method outperforms the state-of-the-art methods.
{"title":"LiDAR-Guided Stereo Matching Using Bayesian Optimization With Gaussian Process Regression","authors":"Hao Yi;Bo Liu;Bin Zhao;Enhai Liu","doi":"10.1109/LGRS.2024.3492175","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3492175","url":null,"abstract":"LiDAR-guided stereo matching for high-precision disparity estimation is a very promising task in photogrammetry and remote sensing. Unfortunately, existing methods suffer from the problem that it is difficult to automatically obtain appropriate stereo matching model parameters to ensure satisfactory results. To solve it, this letter proposes a LiDAR-guided stereo matching framework using Bayesian optimization with Gaussian process regression, which aims to automatically infer the stereo matching model parameters by LiDAR data. First, local matching model based on the belief propagation algorithm is designed. Second, the objective function is constructed by minimizing the difference between the local matching results and the LiDAR data. Third, Bayesian optimization with Gaussian process regression is applied to minimize this objective function to infer the model parameters. Finally, experimental results on the GaoFen-7 and UAV Stereo datasets show that the proposed method can effectively infer suitable model parameters from LiDAR data, and our method outperforms the state-of-the-art methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636271","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 : 2024-11-05DOI: 10.1109/LGRS.2024.3491842
Zhe Geng;Wei Li;Xiang Yu;Daiyin Zhu
Bistatic synthetic aperture radar (SAR) with spatially separated transmitter (TX) and receiver (RX) is advantageous over monostatic SAR systems in trajectory flexibility and antistealth/antijamming capability. On the other hand, since bistatic SAR imaging involves more technical complexities and incurs higher cost, the research in the field of bistatic automatic target recognition (ATR) has been mainly relying on simulated SAR imagery. Reckoning with the lack of supporting database in the public domain, the researchers at Nanjing University of Aeronautics and Astronautics (NUAA) constructed a proprietary bistatic SAR database featuring multiple types of representative military vehicles with the self-developed miniSAR system. Moreover, a multichannel multiview feature fusion network (MMFFN) is devised by incorporating the vision transformer (ViT). The simulation results show that the proposed MMFFN offers a classification accuracy improvement of 4.86%–16.63% over the baseline network (i.e., the plain ViT) in a series of experiments featuring small-to-large observation angle deviations between the training and test data.
{"title":"Bistatic SAR Automatic Target Recognition With Multichannel Multiview Feature Fusion Network","authors":"Zhe Geng;Wei Li;Xiang Yu;Daiyin Zhu","doi":"10.1109/LGRS.2024.3491842","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3491842","url":null,"abstract":"Bistatic synthetic aperture radar (SAR) with spatially separated transmitter (TX) and receiver (RX) is advantageous over monostatic SAR systems in trajectory flexibility and antistealth/antijamming capability. On the other hand, since bistatic SAR imaging involves more technical complexities and incurs higher cost, the research in the field of bistatic automatic target recognition (ATR) has been mainly relying on simulated SAR imagery. Reckoning with the lack of supporting database in the public domain, the researchers at Nanjing University of Aeronautics and Astronautics (NUAA) constructed a proprietary bistatic SAR database featuring multiple types of representative military vehicles with the self-developed miniSAR system. Moreover, a multichannel multiview feature fusion network (MMFFN) is devised by incorporating the vision transformer (ViT). The simulation results show that the proposed MMFFN offers a classification accuracy improvement of 4.86%–16.63% over the baseline network (i.e., the plain ViT) in a series of experiments featuring small-to-large observation angle deviations between the training and test data.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645520","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 : 2024-11-05DOI: 10.1109/LGRS.2024.3490534
Kuo Li;Yushi Chen;Lingbo Huang
Transformer has been widely used in hyperspectral image (HSI) classification tasks because of its ability to capture long-range dependencies. However, most Transformer-based classification methods lack the extraction of local information or do not combine spatial and spectral information well, resulting in insufficient extraction of features. To address these issues, in this study, a dual-branch masked Transformer (Dual-MTr) model is proposed. Masked Transformer (MTr) is used to pretrain vision transformer (ViT) by reconstruction of both masked spatial image and spectral spectrum, which embeds the local bias by the process of recovering from localized patches to the global original input. Different tokenization methods are used for different types of input data. Patch embedding with overlapping regions is used for 2-D spatial data and group embedding is used for 1-D spectral data. Supervised learning has been added to the pretraining process to enhance strong discriminability. Then, the dual-branch structure is proposed to combine the spatial and spectral features. To strengthen the connection between the two branches better, Kullback-Leibler (KL) divergence is used to measure the differences between the classification results of the two branches, and the loss resulting from the computed differences is incorporated into the training process. Experimental results from two hyperspectral datasets demonstrate the effectiveness of the proposed method compared to other methods.
{"title":"Dual Branch Masked Transformer for Hyperspectral Image Classification","authors":"Kuo Li;Yushi Chen;Lingbo Huang","doi":"10.1109/LGRS.2024.3490534","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3490534","url":null,"abstract":"Transformer has been widely used in hyperspectral image (HSI) classification tasks because of its ability to capture long-range dependencies. However, most Transformer-based classification methods lack the extraction of local information or do not combine spatial and spectral information well, resulting in insufficient extraction of features. To address these issues, in this study, a dual-branch masked Transformer (Dual-MTr) model is proposed. Masked Transformer (MTr) is used to pretrain vision transformer (ViT) by reconstruction of both masked spatial image and spectral spectrum, which embeds the local bias by the process of recovering from localized patches to the global original input. Different tokenization methods are used for different types of input data. Patch embedding with overlapping regions is used for 2-D spatial data and group embedding is used for 1-D spectral data. Supervised learning has been added to the pretraining process to enhance strong discriminability. Then, the dual-branch structure is proposed to combine the spatial and spectral features. To strengthen the connection between the two branches better, Kullback-Leibler (KL) divergence is used to measure the differences between the classification results of the two branches, and the loss resulting from the computed differences is incorporated into the training process. Experimental results from two hyperspectral datasets demonstrate the effectiveness of the proposed method compared to other methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636351","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 : 2024-11-04DOI: 10.1109/LGRS.2024.3490732
Haitao Ma;Mengyang Yuan;Ning Wu;Yue Li;Yanan Tian
For subsequent seismic data processing and interpretation, it is important to obtain high-quality distributed acoustic sensing (DAS) signals from down-hole DAS data containing various complex noises. Model-based denoising methods mainly treat this signal estimation issue as a maximum a posteriori (MAP) optimization problem, for its relatively transparent mathematical model and wide range of applications. However, the manually designed prior assumption in MAP cannot accurately describe the actual distribution of DAS data, so the optimization parameters for obtaining high-quality solutions are difficult to determine, making it unavailable in DAS signal estimation. To solve these problems, we propose to emulate the optimization process of MAP with neural networks and accomplish the signal estimation task in feature space via some customized optimization modules. Specifically, we first construct an optimization unit (OPTU) to simulate the optimization process. And then, in order to further obtain the signal distribution of DAS data, we design in each OPTU, a multiscale dense feature aggregation (MDFA) module with the idea of back-projection fusion. With the help of OPTU, the optimization estimation process would be implemented more finely and automatically, expanding the application of MAP for accurate DAS signal estimation. Experiments on both synthetic and field DAS data demonstrate that our method can successfully estimate the high-quality signals from DAS data corrupted by complex noises, with less energy loss.
对于后续的地震数据处理和解释而言，从含有各种复杂噪声的井下分布式声学传感（DAS）数据中获取高质量的分布式声学传感（DAS）信号非常重要。基于模型的去噪方法主要将信号估计问题作为最大后验（MAP）优化问题来处理，其数学模型相对透明，应用范围广泛。然而，MAP 中人工设计的先验假设无法准确描述 DAS 数据的实际分布，因此难以确定获得高质量解的优化参数，导致其在 DAS 信号估计中无法使用。为了解决这些问题，我们提出用神经网络模拟 MAP 的优化过程，通过一些定制的优化模块完成特征空间中的信号估计任务。具体来说，我们首先构建一个优化单元（OPTU）来模拟优化过程。然后，为了进一步获得 DAS 数据的信号分布，我们在每个 OPTU 中设计了一个具有反投影融合思想的多尺度密集特征聚合（MDFA）模块。在 OPTU 的帮助下，优化估计过程将更加精细和自动，从而扩大了 MAP 在准确估计 DAS 信号方面的应用。在合成和现场 DAS 数据上的实验表明，我们的方法可以成功地从被复杂噪声干扰的 DAS 数据中估算出高质量信号，而且能量损失较小。
{"title":"Learning Gradient Descent to Optimize DAS Signal Estimation","authors":"Haitao Ma;Mengyang Yuan;Ning Wu;Yue Li;Yanan Tian","doi":"10.1109/LGRS.2024.3490732","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3490732","url":null,"abstract":"For subsequent seismic data processing and interpretation, it is important to obtain high-quality distributed acoustic sensing (DAS) signals from down-hole DAS data containing various complex noises. Model-based denoising methods mainly treat this signal estimation issue as a maximum a posteriori (MAP) optimization problem, for its relatively transparent mathematical model and wide range of applications. However, the manually designed prior assumption in MAP cannot accurately describe the actual distribution of DAS data, so the optimization parameters for obtaining high-quality solutions are difficult to determine, making it unavailable in DAS signal estimation. To solve these problems, we propose to emulate the optimization process of MAP with neural networks and accomplish the signal estimation task in feature space via some customized optimization modules. Specifically, we first construct an optimization unit (OPTU) to simulate the optimization process. And then, in order to further obtain the signal distribution of DAS data, we design in each OPTU, a multiscale dense feature aggregation (MDFA) module with the idea of back-projection fusion. With the help of OPTU, the optimization estimation process would be implemented more finely and automatically, expanding the application of MAP for accurate DAS signal estimation. Experiments on both synthetic and field DAS data demonstrate that our method can successfully estimate the high-quality signals from DAS data corrupted by complex noises, with less energy loss.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636488","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 : 2024-11-04DOI: 10.1109/LGRS.2024.3491372
Michelle C. A. Picoli;Kenny Helsen
Forest and landscape restoration (FLR) initiatives are essential for combating deforestation, preserving biodiversity, and mitigating climate change. Remote sensing emerges as a key tool in evaluating FLR projects by providing accurate and timely data for monitoring and assessment. This letter presents a framework for generating high-quality maps using remote sensing data to assess the biophysical impact of FLR projects. The framework was applied to evaluate the Katanino FLR Project in Zambia. The results showcase a remarkable increase in forest cover, with a forest classification accuracy exceeding 90%. Such encouraging outcomes underscore the efficacy of the project in achieving its restoration goals and highlight the tangible benefits of employing remote sensing tools in FLR evaluation. Moreover, comprehensive FLR assessment, when complemented with diverse evaluation methodologies, facilitates a holistic understanding of FLR project impacts, enabling informed decision-making for the sustainable management of forest landscapes worldwide.
{"title":"Remote Sensing Framework for Evaluating Forest Landscape Restoration Projects: Enhancing Accuracy and Effectiveness","authors":"Michelle C. A. Picoli;Kenny Helsen","doi":"10.1109/LGRS.2024.3491372","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3491372","url":null,"abstract":"Forest and landscape restoration (FLR) initiatives are essential for combating deforestation, preserving biodiversity, and mitigating climate change. Remote sensing emerges as a key tool in evaluating FLR projects by providing accurate and timely data for monitoring and assessment. This letter presents a framework for generating high-quality maps using remote sensing data to assess the biophysical impact of FLR projects. The framework was applied to evaluate the Katanino FLR Project in Zambia. The results showcase a remarkable increase in forest cover, with a forest classification accuracy exceeding 90%. Such encouraging outcomes underscore the efficacy of the project in achieving its restoration goals and highlight the tangible benefits of employing remote sensing tools in FLR evaluation. Moreover, comprehensive FLR assessment, when complemented with diverse evaluation methodologies, facilitates a holistic understanding of FLR project impacts, enabling informed decision-making for the sustainable management of forest landscapes worldwide.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636250","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 : 2024-11-04DOI: 10.1109/LGRS.2024.3491094
Lianglin Zou;Ping Tang;Yisen Niu;Zixuan Yan;Xilong Lin;Jifeng Song;Qian Wang
The movement of clouds directly influences fluctuations in solar radiation. Therefore, cloud motion vector (CMV) estimation techniques are widely applied in sequential cloud images to predict solar radiation and study other meteorologically related fields. However, traditional block matching, optical flow, and feature point methods struggle to accurately capture the deformation, multilayered, and mixed cloud types’ motion due to the lack of deep semantic understanding of cloud images. Additionally, without cloud-motion-labeled, deep learning tools such as CNNs are limited in their utility for motion assessment. Therefore, this letter proposes a method of cloud image depth feature matching to assess the CMV in time series, including image enhancement, self-supervised feature extraction, feature matching, feature fusion, and spatiotemporal filtering. Experimental results demonstrate a significant improvement in accuracy compared to traditional CMV estimation techniques, with higher robustness observed across various complex cloud scenarios.
{"title":"A Cloud Motion Estimation Method Based on Cloud Image Depth Feature Matching","authors":"Lianglin Zou;Ping Tang;Yisen Niu;Zixuan Yan;Xilong Lin;Jifeng Song;Qian Wang","doi":"10.1109/LGRS.2024.3491094","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3491094","url":null,"abstract":"The movement of clouds directly influences fluctuations in solar radiation. Therefore, cloud motion vector (CMV) estimation techniques are widely applied in sequential cloud images to predict solar radiation and study other meteorologically related fields. However, traditional block matching, optical flow, and feature point methods struggle to accurately capture the deformation, multilayered, and mixed cloud types’ motion due to the lack of deep semantic understanding of cloud images. Additionally, without cloud-motion-labeled, deep learning tools such as CNNs are limited in their utility for motion assessment. Therefore, this letter proposes a method of cloud image depth feature matching to assess the CMV in time series, including image enhancement, self-supervised feature extraction, feature matching, feature fusion, and spatiotemporal filtering. Experimental results demonstrate a significant improvement in accuracy compared to traditional CMV estimation techniques, with higher robustness observed across various complex cloud scenarios.","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":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691739","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 : 2024-11-04DOI: 10.1109/LGRS.2024.3490552
Baojing Zhang;Zhiyong Wang;Zhenjin Li;Wenfu Yang;Weibing Li
Due to the large deformation gradient caused by mining, it is easy to cause serious incoherence phenomenon in radar interferometry, and the traditional phase unwrapping (PU) method is limited in this case. To solve this problem, a novel PU method for mining area based on edge detection using the SegNet model is proposed for mining subsidence basins with large deformation. First, SegNet network was used to extract the edge information of the subsidence basin in the mining area. Then, the edges were refined and connected by the Zhang-Suen thinning method and regional growth method, respectively. Finally, PU was completed by the determined phase jump variables. Simulated interferograms with different signal-to-noise ratio (SNR) and two real interferograms with different interference qualities are selected for experiments. Compared with the three traditional PU methods and two deep learning PU methods, the proposed model has higher accuracy and better robustness. When the SNR is 1 and 4, the unwrapping error distribution area of the proposed method is the smallest, and the PU result is more close to the real situation in the interferogram of real mining area. The novel two-step PU method effectively solves the problem that the traditional PU method is seriously affected by noise and large deformation.
由于采矿造成的大变形梯度，在雷达干涉测量中容易造成严重的不相干现象，传统的相位解包（PU）方法在这种情况下受到限制。为解决这一问题，针对具有较大变形的采矿沉陷盆地，提出了一种基于边缘检测的 SegNet 模型的新型采空区 PU 方法。首先，使用 SegNet 网络提取采矿区沉陷盆地的边缘信息。然后，分别采用张-孙稀疏法和区域增长法对边缘进行细化和连接。最后，通过确定的相跃变量完成 PU。实验选取了不同信噪比（SNR）的模拟干涉图和两个不同干扰质量的真实干涉图。与三种传统 PU 方法和两种深度学习 PU 方法相比，所提出的模型具有更高的精度和更好的鲁棒性。当信噪比为 1 和 4 时，所提方法的解包误差分布区域最小，在真实矿区干涉图中的 PU 结果更接近真实情况；当信噪比为 1 和 4 时，所提方法的解包误差分布区域最小，在真实矿区干涉图中的 PU 结果更接近真实情况。新颖的两步 PU 方法有效地解决了传统 PU 方法受噪声和大变形影响严重的问题。
{"title":"A Novel PU Method for Mining Area Based on Edge Detection Using the SegNet Model","authors":"Baojing Zhang;Zhiyong Wang;Zhenjin Li;Wenfu Yang;Weibing Li","doi":"10.1109/LGRS.2024.3490552","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3490552","url":null,"abstract":"Due to the large deformation gradient caused by mining, it is easy to cause serious incoherence phenomenon in radar interferometry, and the traditional phase unwrapping (PU) method is limited in this case. To solve this problem, a novel PU method for mining area based on edge detection using the SegNet model is proposed for mining subsidence basins with large deformation. First, SegNet network was used to extract the edge information of the subsidence basin in the mining area. Then, the edges were refined and connected by the Zhang-Suen thinning method and regional growth method, respectively. Finally, PU was completed by the determined phase jump variables. Simulated interferograms with different signal-to-noise ratio (SNR) and two real interferograms with different interference qualities are selected for experiments. Compared with the three traditional PU methods and two deep learning PU methods, the proposed model has higher accuracy and better robustness. When the SNR is 1 and 4, the unwrapping error distribution area of the proposed method is the smallest, and the PU result is more close to the real situation in the interferogram of real mining area. The novel two-step PU method effectively solves the problem that the traditional PU method is seriously affected by noise and large deformation.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645543","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}
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