Pub Date : 2025-03-27DOI: 10.1109/LGRS.2025.3555259
Yvyang Gao;Ganggang Dong
Frequency estimation was a fundamental problem in radar imaging. The classical Fourier spectral analysis suffered from the Rayleigh limit. The imaging performance deteriorated rapidly in low SNR conditions. In addition, the prior knowledge on the number of signal sources was required. To solve the problems, a new data-driven spectral estimation method via frequency attention fusion (FreqAF) was proposed in this letter. Different from the preceding works, the signal spectral were estimated by a deep architecture neural network automatically. The echo signal was first dechirped according to the radar parameters. It is then fed into a deep architecture for spectral estimation. The proposed architecture was composed of three phases, the decomposition, the FreqAF, and the projection. In the decomposition phase, the individual single-frequency components were estimated from the input dechirped signal. The components were dynamically fused in a delicate FreqAF module. The frequencies were obtained finally in the projection phase. Numerical experiments are performed to verify the proposed method.
{"title":"FreqAF: A New Frequency Attention Fusion Spectral Estimation Method for Radar Super-Resolution Imaging","authors":"Yvyang Gao;Ganggang Dong","doi":"10.1109/LGRS.2025.3555259","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3555259","url":null,"abstract":"Frequency estimation was a fundamental problem in radar imaging. The classical Fourier spectral analysis suffered from the Rayleigh limit. The imaging performance deteriorated rapidly in low SNR conditions. In addition, the prior knowledge on the number of signal sources was required. To solve the problems, a new data-driven spectral estimation method via frequency attention fusion (FreqAF) was proposed in this letter. Different from the preceding works, the signal spectral were estimated by a deep architecture neural network automatically. The echo signal was first dechirped according to the radar parameters. It is then fed into a deep architecture for spectral estimation. The proposed architecture was composed of three phases, the decomposition, the FreqAF, and the projection. In the decomposition phase, the individual single-frequency components were estimated from the input dechirped signal. The components were dynamically fused in a delicate FreqAF module. The frequencies were obtained finally in the projection phase. Numerical experiments are performed to verify the proposed method.","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":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786355","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 : 2025-03-24DOI: 10.1109/LGRS.2025.3554296
Haiqiang Chen;Yongxiang Chen;Zhenchang Zhang
Accurate prediction of sea surface temperature (SST) plays a critical role in climate research and marine ecosystem management. Traditional models predict trends by analyzing and fitting data, but they struggle with capturing long-range dependencies and complex spatiotemporal patterns. The transformer’s attention mechanism effectively addresses long-range dependencies, but its high computational complexity poses challenges. To overcome these limitations, this study proposes a novel spatiotemporal sequence prediction model: the spatiotemporal vision mamba recurrent neural network (SVRNN). The model innovatively integrates a bidirectional state-space processing mechanism and decoupled memory modules. The bidirectional mechanism maintains a global receptive field with linear computational complexity, while the decoupled memory modules explicitly separate spatiotemporal dependencies, enhancing the model’s ability to capture complex spatiotemporal patterns. During the experiment on hourly SST prediction in the Taiwan Strait, where the SST of the next 12 h was predicted using data from the previous 12 h, the SVRNN model demonstrated superior performance, achieving a root mean square error (RMSE) of $0.159~^{circ }$ C, a mean absolute error (MAE) of $0.105~^{circ }$ C, and a mean absolute percentage error (MAPE) of 0.496%. Furthermore, our seasonal error analysis reveals that the model exhibits robust performance in different seasons, providing more reliable technical support for SST prediction in Taiwan Strait.
{"title":"SVRNN: A Spatiotemporal Prediction Model for Sea Surface Temperature Prediction in the Taiwan Strait","authors":"Haiqiang Chen;Yongxiang Chen;Zhenchang Zhang","doi":"10.1109/LGRS.2025.3554296","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554296","url":null,"abstract":"Accurate prediction of sea surface temperature (SST) plays a critical role in climate research and marine ecosystem management. Traditional models predict trends by analyzing and fitting data, but they struggle with capturing long-range dependencies and complex spatiotemporal patterns. The transformer’s attention mechanism effectively addresses long-range dependencies, but its high computational complexity poses challenges. To overcome these limitations, this study proposes a novel spatiotemporal sequence prediction model: the spatiotemporal vision mamba recurrent neural network (SVRNN). The model innovatively integrates a bidirectional state-space processing mechanism and decoupled memory modules. The bidirectional mechanism maintains a global receptive field with linear computational complexity, while the decoupled memory modules explicitly separate spatiotemporal dependencies, enhancing the model’s ability to capture complex spatiotemporal patterns. During the experiment on hourly SST prediction in the Taiwan Strait, where the SST of the next 12 h was predicted using data from the previous 12 h, the SVRNN model demonstrated superior performance, achieving a root mean square error (RMSE) of <inline-formula> <tex-math>$0.159~^{circ }$ </tex-math></inline-formula>C, a mean absolute error (MAE) of <inline-formula> <tex-math>$0.105~^{circ }$ </tex-math></inline-formula>C, and a mean absolute percentage error (MAPE) of 0.496%. Furthermore, our seasonal error analysis reveals that the model exhibits robust performance in different seasons, providing more reliable technical support for SST prediction in Taiwan Strait.","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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783287","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}
As more and more multispectral and hyperspectral platforms are deployed for Earth observation (EO), limited downlink capacity increases the pressure for more efficient data compression algorithms. Machine learning (ML) has been successfully applied to produce highly competitive compression models though this performance has typically been at the cost of high computational complexity, a crucial limitation for on-board remote sensing data compression. To address these issues, a reduced-complexity multispectral and hyperspectral data compression architecture is proposed. Using separate spectral and spatial transforms, the complexity of the proposed models is scalable on the number of bands, regardless of the compression ratios. This proposal outperforms state-of-the-art ML compression models as well as established lossy compression methods such as JPEG 2000 prepended with a spectral Karhunen-Loève transform (KLT) on a variety of remote sensing data sources. The performance improvement is achieved with a lower complexity than said ML models. To reproduce our results, training and test data are publicly available at https://gici.uab.cat/GiciWebPage/datasets.php and source code at https://github.com/smijares/mbhs2025.
随着用于地球观测（EO）的多光谱和高光谱平台越来越多，有限的下行链路容量增加了采用更高效数据压缩算法的压力。机器学习（ML）已被成功应用于生产极具竞争力的压缩模型，但这种性能通常是以高计算复杂性为代价的，而计算复杂性是机载遥感数据压缩的一个关键限制因素。为了解决这些问题，我们提出了一种降低复杂度的多光谱和高光谱数据压缩架构。利用单独的光谱和空间变换，无论压缩率如何，所提模型的复杂度都可以根据波段数量进行扩展。在各种遥感数据源上，该建议的性能优于最先进的 ML 压缩模型和已有的有损压缩方法，如在 JPEG 2000 中预置光谱卡尔胡宁-洛埃夫变换（KLT）。与上述 ML 模型相比，该方法的复杂度更低，但性能却有所提高。为了重现我们的成果，训练和测试数据可在 https://gici.uab.cat/GiciWebPage/datasets.php 网站上公开，源代码可在 https://github.com/smijares/mbhs2025 网站上公开。
{"title":"Learned Spectral and Spatial Transforms for Multispectral Remote Sensing Data Compression","authors":"Sebastià Mijares;Joan Bartrina-Rapesta;Miguel Hernández-Cabronero;Joan Serra-Sagristà","doi":"10.1109/LGRS.2025.3554269","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554269","url":null,"abstract":"As more and more multispectral and hyperspectral platforms are deployed for Earth observation (EO), limited downlink capacity increases the pressure for more efficient data compression algorithms. Machine learning (ML) has been successfully applied to produce highly competitive compression models though this performance has typically been at the cost of high computational complexity, a crucial limitation for on-board remote sensing data compression. To address these issues, a reduced-complexity multispectral and hyperspectral data compression architecture is proposed. Using separate spectral and spatial transforms, the complexity of the proposed models is scalable on the number of bands, regardless of the compression ratios. This proposal outperforms state-of-the-art ML compression models as well as established lossy compression methods such as JPEG 2000 prepended with a spectral Karhunen-Loève transform (KLT) on a variety of remote sensing data sources. The performance improvement is achieved with a lower complexity than said ML models. To reproduce our results, training and test data are publicly available at <uri>https://gici.uab.cat/GiciWebPage/datasets.php</uri> and source code at <uri>https://github.com/smijares/mbhs2025</uri>.","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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777774","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 : 2025-03-24DOI: 10.1109/LGRS.2025.3554356
Dongkai Yan;Xudong Sun;Jiahua Zhang;Xiaodi Shang
Band selection (BS) is a vital technique for improving efficiency of hyperspectral image (HSI) processing. This letter proposes a dual-view structural similarity subspace clustering model (DVS3C) for BS. Traditional low-rank subspace clustering (LRSC) methods rely solely on single-view data (e.g., original HSI), potentially leading to the loss of critical information (e.g., spatial structures) and insufficient exploitation of the multi-dimensional features of HSI for optimal BS. To do so, DVS3C constructs a spatial view alongside the spectral view, leveraging global spectral-spatial information through subspace clustering to achieve complementary advantages between views. Besides, to overcome LRSC’s limitations in capturing band local structure, DVS3C introduces a structural similarity matrix to deeply exploit intraview neighborhood relationships of bands, further reducing band redundancy. Ultimately, an adaptive dual-view fusion strategy that iteratively optimizes a consensus matrix while dynamically adjusting the contribution of each view is designed to ensure view consistency. Experimental results on four public datasets demonstrate its remarkable stability and superiority. The source code is available at https://github.com/ydk0912/DVS3C.
{"title":"Dual-View Structural Similarity Subspace Clustering for Hyperspectral Band Selection","authors":"Dongkai Yan;Xudong Sun;Jiahua Zhang;Xiaodi Shang","doi":"10.1109/LGRS.2025.3554356","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554356","url":null,"abstract":"Band selection (BS) is a vital technique for improving efficiency of hyperspectral image (HSI) processing. This letter proposes a dual-view structural similarity subspace clustering model (DVS3C) for BS. Traditional low-rank subspace clustering (LRSC) methods rely solely on single-view data (e.g., original HSI), potentially leading to the loss of critical information (e.g., spatial structures) and insufficient exploitation of the multi-dimensional features of HSI for optimal BS. To do so, DVS3C constructs a spatial view alongside the spectral view, leveraging global spectral-spatial information through subspace clustering to achieve complementary advantages between views. Besides, to overcome LRSC’s limitations in capturing band local structure, DVS3C introduces a structural similarity matrix to deeply exploit intraview neighborhood relationships of bands, further reducing band redundancy. Ultimately, an adaptive dual-view fusion strategy that iteratively optimizes a consensus matrix while dynamically adjusting the contribution of each view is designed to ensure view consistency. Experimental results on four public datasets demonstrate its remarkable stability and superiority. The source code is available at <uri>https://github.com/ydk0912/DVS3C</uri>.","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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786388","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 : 2025-03-17DOI: 10.1109/LGRS.2025.3551754
Yan Xing;Yunan Jia;Sen Gao;Jiali Hu;Rui Huang
Remote sensing (RS) change detection (CD) is a critical task in monitoring surface dynamics. Recently, Mamba-based methods have shown promising performance and are quickly adopted in change detection. However, when addressing the task of CD in complex scenarios, existing methods have limitations in capturing features of minor and texture changes due to the lack of frequency information. To address these challenges, we propose a frequency-enhanced Mamba for RSCD (FEMCD). First, we design a difference-guided state-space model (DGSSM) to extract change-related features. DGSSM takes the features of bitemporal images as input and uses absolute-difference features to guide the network to focus on change regions. Second, we develop a DCT-aided Mamba decoder (DCTMD) for feature decoding and refinement. DCTMD uses the omnidirectional selective scan module (OSSM) to refine the change-related features and DCT to capture minor change details. Finally, we use a simple classifier to generate the final change map. We have conducted extensive experiments on five RSCD datasets, comparing FEMCD with 11 SOTA change detectors. The experimental results show that our proposed FEMCD method outperforms other compared methods. The code can be found at: https://github.com/JYN712/FEMCD.
{"title":"Frequency-Enhanced Mamba for Remote Sensing Change Detection","authors":"Yan Xing;Yunan Jia;Sen Gao;Jiali Hu;Rui Huang","doi":"10.1109/LGRS.2025.3551754","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3551754","url":null,"abstract":"Remote sensing (RS) change detection (CD) is a critical task in monitoring surface dynamics. Recently, Mamba-based methods have shown promising performance and are quickly adopted in change detection. However, when addressing the task of CD in complex scenarios, existing methods have limitations in capturing features of minor and texture changes due to the lack of frequency information. To address these challenges, we propose a frequency-enhanced Mamba for RSCD (FEMCD). First, we design a difference-guided state-space model (DGSSM) to extract change-related features. DGSSM takes the features of bitemporal images as input and uses absolute-difference features to guide the network to focus on change regions. Second, we develop a DCT-aided Mamba decoder (DCTMD) for feature decoding and refinement. DCTMD uses the omnidirectional selective scan module (OSSM) to refine the change-related features and DCT to capture minor change details. Finally, we use a simple classifier to generate the final change map. We have conducted extensive experiments on five RSCD datasets, comparing FEMCD with 11 SOTA change detectors. The experimental results show that our proposed FEMCD method outperforms other compared methods. The code can be found at: <uri>https://github.com/JYN712/FEMCD</uri>.","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":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769548","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 : 2025-03-17DOI: 10.1109/LGRS.2025.3552055
Seung-Jae Lee;Sun-Gu Lee
In this study, we utilize the population-based optimization (PBO) techniques to accurately retrieve the forest height (FH) in polarimetric synthetic aperture radar interferometry (PolInSAR) inversion. After the initial FH information is obtained using conventional PolInSAR inversion methods, it is adjusted using the PBO techniques and two physical models, which are the random-volume-over ground (RVoG) and the simplified version of random-motion-over-ground (RMoG) models. The concept of fine-tuning was applied to both single-baseline (SB) and multibaseline (MB) PolInSAR data. In the results obtained using both the simulated and real data, the proposed fine-tuning approach exhibits significantly improved FH estimation results, as compared with the conventional inversions.
{"title":"Fine-Tuning of Forest Height Retrieval in PolInSAR Using Population-Based Optimization","authors":"Seung-Jae Lee;Sun-Gu Lee","doi":"10.1109/LGRS.2025.3552055","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3552055","url":null,"abstract":"In this study, we utilize the population-based optimization (PBO) techniques to accurately retrieve the forest height (FH) in polarimetric synthetic aperture radar interferometry (PolInSAR) inversion. After the initial FH information is obtained using conventional PolInSAR inversion methods, it is adjusted using the PBO techniques and two physical models, which are the random-volume-over ground (RVoG) and the simplified version of random-motion-over-ground (RMoG) models. The concept of fine-tuning was applied to both single-baseline (SB) and multibaseline (MB) PolInSAR data. In the results obtained using both the simulated and real data, the proposed fine-tuning approach exhibits significantly improved FH estimation results, as compared with the conventional inversions.","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":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769526","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 : 2025-03-14DOI: 10.1109/LGRS.2025.3551235
Jianfei Chen;Jiahao Yu;Yujie Ruan;Chenggong Zhang;Ziang Zheng;Fuxin Cai;Shujin Zhu;Leilei Liu
The Synthetic Aperture Interferometric Radiometer (SAIR) can realize high-resolution real-time imaging observation by using aperture synthesis technology, which has strong application advantages in the field of earth remote sensing and radio astronomy. However, due to the limitation of engineering technology, the aperture of the SAIR is still limited, which limits the further improvement of SAIR’s spatial resolution. Therefore, this letter proposes a novel super-resolution imaging method based on spectral extrapolation network (SR-SEN), which can further improve the SAIR’s imaging resolution without increasing the system hardware scale. In the SR-SEN method, the spectral extrapolation subnet is used to deduce the high-frequency spectral components from the low-frequency visibility function measured by the SAIR system, and the iterative reconstruction subnet is constructed to realize the super-resolution imaging inversion of the target scene. The simulation results show that the proposed SR-SEN method can realize accurate spectral extrapolation, improve the imaging resolution of SAIR, and finally realize high-quality imaging inversion.
{"title":"Super-Resolution Imaging Method for Synthetic Aperture Interferometric Radiometer Based on Spectral Extrapolation","authors":"Jianfei Chen;Jiahao Yu;Yujie Ruan;Chenggong Zhang;Ziang Zheng;Fuxin Cai;Shujin Zhu;Leilei Liu","doi":"10.1109/LGRS.2025.3551235","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3551235","url":null,"abstract":"The Synthetic Aperture Interferometric Radiometer (SAIR) can realize high-resolution real-time imaging observation by using aperture synthesis technology, which has strong application advantages in the field of earth remote sensing and radio astronomy. However, due to the limitation of engineering technology, the aperture of the SAIR is still limited, which limits the further improvement of SAIR’s spatial resolution. Therefore, this letter proposes a novel super-resolution imaging method based on spectral extrapolation network (SR-SEN), which can further improve the SAIR’s imaging resolution without increasing the system hardware scale. In the SR-SEN method, the spectral extrapolation subnet is used to deduce the high-frequency spectral components from the low-frequency visibility function measured by the SAIR system, and the iterative reconstruction subnet is constructed to realize the super-resolution imaging inversion of the target scene. The simulation results show that the proposed SR-SEN method can realize accurate spectral extrapolation, improve the imaging resolution of SAIR, and finally realize high-quality imaging inversion.","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":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716479","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}
Detecting rice transplantation dates is crucial for understanding its effect on grain yield and water consumption at regional scales. Traditionally, identifying the rice transplantation phase using dual-polarized (dual-pol) synthetic aperture radar (SAR) data has relied on backscatter intensity due to its characteristic low values during the flooding stage. This study leverages a recently proposed dual-pol radar surface index (DpRSI) to analyze the spatiotemporal dynamics of the rice transplantation phases. Using this index, we propose an unsupervised framework to identify rice transplantation dates. The framework is evaluated using ground-truth (GT) data over rice-cultivated regions in Vijayawada, India, during the kharif season 2018, demonstrating its effectiveness in detecting shifts in transplantation dates over a large spatial extent.
{"title":"A Dual-Pol SAR-Based Index for Rice Transplantation Detection","authors":"Abhinav Verma;Avik Bhattacharya;Dipankar Mandal;Carlos López-Martínez;Paolo Gamba","doi":"10.1109/LGRS.2025.3550971","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3550971","url":null,"abstract":"Detecting rice transplantation dates is crucial for understanding its effect on grain yield and water consumption at regional scales. Traditionally, identifying the rice transplantation phase using dual-polarized (dual-pol) synthetic aperture radar (SAR) data has relied on backscatter intensity due to its characteristic low values during the flooding stage. This study leverages a recently proposed dual-pol radar surface index (DpRSI) to analyze the spatiotemporal dynamics of the rice transplantation phases. Using this index, we propose an unsupervised framework to identify rice transplantation dates. The framework is evaluated using ground-truth (GT) data over rice-cultivated regions in Vijayawada, India, during the kharif season 2018, demonstrating its effectiveness in detecting shifts in transplantation dates over a large spatial extent.","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":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761380","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 : 2025-03-13DOI: 10.1109/LGRS.2025.3550967
Juan Pablo Navarro Castillo;Rolf Scheiber;Marc Jäger;Alberto Moreira
High-resolution wide swath (HRWS) synthetic aperture radar (SAR) systems are normally designed to have identical antenna patterns in each receive channel. Nevertheless, due to different factors, this condition might not be satisfied, resulting in a multichannel radar system with different antenna patterns. This letter studies the impact of these relative antenna differences on the performance of a state-of-the-art azimuth motion-adaptive image reconstruction for a real airborne SAR sensor with multiple azimuth channels on receive. The performance of the reconstruction algorithm is evaluated both when these relative differences are neglected and when they are accounted for in the multichannel reconstruction process. Additionally, the range dependence of the antenna pattern as a function of wavenumber and the use of range block processing to minimize the impact of this dependence are analyzed. The results confirm the relevance of including these additional steps in the reconstruction, extending the understanding of SAR systems using digital beamforming (DBF) in azimuth.
{"title":"Interchannel Antenna Pattern Correction for Azimuth Digital Beamforming in Airborne SAR","authors":"Juan Pablo Navarro Castillo;Rolf Scheiber;Marc Jäger;Alberto Moreira","doi":"10.1109/LGRS.2025.3550967","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3550967","url":null,"abstract":"High-resolution wide swath (HRWS) synthetic aperture radar (SAR) systems are normally designed to have identical antenna patterns in each receive channel. Nevertheless, due to different factors, this condition might not be satisfied, resulting in a multichannel radar system with different antenna patterns. This letter studies the impact of these relative antenna differences on the performance of a state-of-the-art azimuth motion-adaptive image reconstruction for a real airborne SAR sensor with multiple azimuth channels on receive. The performance of the reconstruction algorithm is evaluated both when these relative differences are neglected and when they are accounted for in the multichannel reconstruction process. Additionally, the range dependence of the antenna pattern as a function of wavenumber and the use of range block processing to minimize the impact of this dependence are analyzed. The results confirm the relevance of including these additional steps in the reconstruction, extending the understanding of SAR systems using digital beamforming (DBF) in azimuth.","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":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716470","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}
In this letter, an Inverse synthetic aperture radar (ISAR) imaging method uses second harmonic signals to extract motion parameters of multi-rotor autonomous aerial vehicles in an environment containing other natural objects for high-precision ISAR imaging. The core of the drones, which include powerful radio frequency (RF) circuits, is proven to have strong nonlinear effects. Translational motion parameters are estimated from prominent points in second harmonic signals, and the rotational motion of the drone is corrected by instantaneous imaging. An integrated RF system commonly used in drones has been tested, and its nonlinear phase characteristics have been obtained. The real phase error due to the nonlinearity was carried over into the subsequent simulation and compensated, which did not lead to defocusing in the final image. The feasibility of using second harmonic signals to compensate for fundamental frequency motion errors is verified by simulations. The micro-Doppler effect, generated by the high-speed rotation of the blades, has been demonstrated in the simulation images. System integration with a harmonic signal-based motion correction (H-MC) approach for motion compensation is shown to demonstrate a solution for accurate imaging for drones.
{"title":"High-Precision ISAR Method for Nonlinear Rotating Targets: Imaging for Drones","authors":"Chenhao Zhao;Qinghai Dong;Bingnan Wang;Maosheng Xiang","doi":"10.1109/LGRS.2025.3547441","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3547441","url":null,"abstract":"In this letter, an Inverse synthetic aperture radar (ISAR) imaging method uses second harmonic signals to extract motion parameters of multi-rotor autonomous aerial vehicles in an environment containing other natural objects for high-precision ISAR imaging. The core of the drones, which include powerful radio frequency (RF) circuits, is proven to have strong nonlinear effects. Translational motion parameters are estimated from prominent points in second harmonic signals, and the rotational motion of the drone is corrected by instantaneous imaging. An integrated RF system commonly used in drones has been tested, and its nonlinear phase characteristics have been obtained. The real phase error due to the nonlinearity was carried over into the subsequent simulation and compensated, which did not lead to defocusing in the final image. The feasibility of using second harmonic signals to compensate for fundamental frequency motion errors is verified by simulations. The micro-Doppler effect, generated by the high-speed rotation of the blades, has been demonstrated in the simulation images. System integration with a harmonic signal-based motion correction (H-MC) approach for motion compensation is shown to demonstrate a solution for accurate imaging for drones.","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":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716495","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：