ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"A novel deep learning algorithm for broad scale seagrass extent mapping in shallow coastal environments","authors":"Jianghai Peng ,&nbsp;Jiwei Li ,&nbsp;Thomas C. Ingalls ,&nbsp;Steven R. Schill ,&nbsp;Hannah R. Kerner ,&nbsp;Gregory P. Asner","doi":"10.1016/j.isprsjprs.2024.12.008","DOIUrl":"10.1016/j.isprsjprs.2024.12.008","url":null,"abstract":"<div><div>Recently, the importance of seagrasses in the functioning of coastal ecosystems and their ability to mitigate climate change has gained increased recognition. However, there has been a rapid global deterioration of seagrass ecosystems due to climate change and human-mediated disturbances. Accurate broad-scale mapping of seagrass extent is necessary for seagrass conservation and management actions. Traditionally, these mapping methods have primarily relied on spectral information, along with additional data such as manually designed spatial/texture features (e.g., from the Gray Level Co-Occurrence Matrix) and satellite-derived bathymetry. Despite the widely reported success of prior methods in mapping seagrass across small geographic areas, two challenges remain in broad-scale seagrass extent mapping: 1) spectral overlap between seagrass and other benthic habitats that results in the misclassification of coral/macroalgae to seagrass; 2) seagrass ecosystems exhibit spatial and temporal variability, most current models trained on data from specific locations or time periods encounter difficulties in generalizing to diverse locations or time periods with varying seagrass characteristics, such as density and species. In this study, we developed a novel deep learning model (i.e., Seagrass DenseNet: SGDenseNet) based on the DenseNet architecture to overcome these difficulties. The model was trained and validated using surface reflectance from Sentinel-2 MSI and 9,369 field data samples from four diverse regional shallow coastal water areas. Our model achieves an overall accuracy of 90% for seagrass extent mapping. Furthermore, we evaluated our deep learning model using 1,067 seagrass field data samples worldwide, achieving a producer’s accuracy of 81%. Our new deep learning model could be applied to map seagrass extents at a very broad-scale with high accuracy.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 277-294"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a spatiotemporal fusion framework for generating daily UAV images in agricultural areas using publicly available satellite data","authors":"Hamid Ebrahimy,&nbsp;Tong Yu,&nbsp;Zhou Zhang","doi":"10.1016/j.isprsjprs.2024.12.024","DOIUrl":"10.1016/j.isprsjprs.2024.12.024","url":null,"abstract":"<div><div>Monitoring agricultural areas, given their rapid transformation and small-scale spatial changes, necessitates obtaining dense time series of high-resolution remote sensing data. In this manner, the unmanned aerial vehicle (UAV) that can provide high-resolution images is indispensable for monitoring and assessing agricultural areas, especially for rapidly changing crops like alfalfa. Considering the practical limitations of acquiring daily UAV images, the utilization of spatiotemporal fusion (STF) approaches to integrate publicly available satellite images with high temporal resolution and UAV images with high spatial resolution can be considered an effective alternative. This study proposed an effective STF algorithm that utilizes the Generalized Linear Model (GLM) as the mapping function and is called GLM-STF. The algorithm is designed to use coarse difference images to map fine difference images via the GLM algorithm. It then combines these fine difference images with the original fine images to synthesize daily UAV image at the prediction time. In this study, we deployed a two-step STF process: (1) MODIS MCD43A4 and Harmonized Landsat and Sentinel-2 (HLS) data were fused to produce daily HLS images; and (2) daily HLS data and UAV images were fused to produce daily UAV images. We evaluated the reliability of the deployed framework at three distinct experimental sites that were covered by alfalfa crops. The performance of the GLM-STF algorithm was compared with five benchmark STF algorithms: STARFM, ESTARFM, Fit-FC, FSDAF, and VSDF, by using three quantitative accuracy evaluation metrics, including root mean squared error (RMSE), correlation coefficient (CC), and structure similarity index (SSIM). The proposed STF algorithm yielded the most accurate synthesized UAV images, followed by VSDF, which proved to be the most accurate benchmark algorithm. Specifically, GML-STF achieved an average RMSE of 0.029 (compared to VSDF’s 0.043), an average CC of 0.725 (compared to VSDF’s 0.669), and an average SSIM of 0.840 (compared to VSDF’s 0.811). The superiority of GLM-STF was also observed with the visual comparisons as well. Additionally, GLM-STF was less sensitive to the increase in the acquisition time difference between the reference image pairs and prediction date, indicating its suitability for STF tasks with limited input reference pairs. The developed framework in this study is thus expected to provide high-quality UAV images with high spatial resolution and frequent observations for various applications.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 413-427"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3LATNet: Attention based deep learning model for global Chlorophyll-a retrieval from GCOM-C satellite","authors":"Muhammad Salah ,&nbsp;Salem Ibrahim Salem ,&nbsp;Nobuyuki Utsumi ,&nbsp;Hiroto Higa ,&nbsp;Joji Ishizaka ,&nbsp;Kazuo Oki","doi":"10.1016/j.isprsjprs.2024.12.019","DOIUrl":"10.1016/j.isprsjprs.2024.12.019","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Chlorophyll-a (Chla) retrieval from satellite observations is crucial for assessing water quality and the health of aquatic ecosystems. Utilizing satellite data, while invaluable, poses challenges including inherent satellite biases, the necessity for precise atmospheric correction (AC), and the complexity of water bodies, all of which complicate establishing a reliable relationship between remote sensing reflectance (R&lt;sub&gt;rs&lt;/sub&gt;) and Chla concentrations. Furthermore, the Global Change Observation Mission − Climate (GCOM-C) satellite operated by Japan Aerospace Exploration Agency (JAXA) has brought a significant leap forward in ocean color monitoring, featuring a 250 m spatial resolution and integrating the 380 nm band, enhancing the detection capabilities for aquatic environments. JAXA’s standard Chla product grounded in empirical algorithms, coupled with the limited research on the impact of atmospheric correction (AC) on R&lt;sub&gt;rs&lt;/sub&gt; products, underscores the need for further analysis of these factors. This study introduces the three bidirectional Long short–term memory and ATtention mechanism Network (3LATNet) model that was trained on a large dataset incorporating 5610 in-situ R&lt;sub&gt;rs&lt;/sub&gt; measurements and their corresponding Chla concentrations collected from global locations to cover broad trophic status. The R&lt;sub&gt;rs&lt;/sub&gt; spectra have been resampled to the Second-Generation Global Imager (SGLI) aboard GCOM-C. The model was also trained using satellite matchup data, aiming to achieve a generalized deep-learning model. 3LATNet was evaluated compared to conventional Chla algorithms and ML algorithms, including JAXA’s standard Chla product. Our findings reveal a remarkable reduction in Chla estimation error, marked by a 42.5 % (from 17 to 9.77 mg/m&lt;sup&gt;3&lt;/sup&gt;) reduction in mean absolute error (MAE) and a 57.3 % (from 43.12 to 18.43 mg/m&lt;sup&gt;3&lt;/sup&gt;) reduction in root mean square error (RMSE) compared to JAXA’s standard Chla algorithm using in-situ data, and nearly a twofold improvement in absolute errors when evaluating using matchup SGLI R&lt;sub&gt;rs&lt;/sub&gt;. Furthermore, we conduct an in-depth assessment of the impact of AC on the models’ performance. SeaDAS predominantly exhibited invalid reflectance values at the 412 nm band, while OC-SMART displayed more significant variability in percentage errors. In comparison, JAXA’s AC proved more precise in retrieving R&lt;sub&gt;rs&lt;/sub&gt;. We comprehensively evaluated the spatial consistency of Chla models under clear and harmful algal bloom events. 3LATNet effectively captured Chla patterns across various ranges. Conversely, the RF algorithm frequently overestimates Chla concentrations in the low to mid-range. JAXA’s Chla algorithm, on the other hand, consistently tends to underestimate Chla concentrations, a trend that is particularly pronounced in high-range Chla areas and during harmful algal bloom events. These outcomes underscore the potential of our innovative approach for enhancing g","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 490-508"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PolSAR2PolSAR: A semi-supervised despeckling algorithm for polarimetric SAR images","authors":"Cristiano Ulondu Mendes ,&nbsp;Emanuele Dalsasso ,&nbsp;Yi Zhang ,&nbsp;Loïc Denis ,&nbsp;Florence Tupin","doi":"10.1016/j.isprsjprs.2025.01.008","DOIUrl":"10.1016/j.isprsjprs.2025.01.008","url":null,"abstract":"<div><div>Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a valuable tool for Earth observation. This imaging technique finds wide application in various fields, including agriculture, forestry, geology, and disaster monitoring. However, due to the inherent presence of speckle noise, filtering is often necessary to improve the interpretability and reliability of PolSAR data. The effectiveness of a speckle filter is measured by its ability to attenuate fluctuations without introducing artifacts or degrading spatial and polarimetric information. Recent advancements in this domain leverage the power of deep learning. These approaches adopt a supervised learning strategy, which requires a large amount of speckle-free images that are costly to produce. In contrast, this paper presents PolSAR2PolSAR, a semi-supervised learning strategy that only requires, from the sensor under consideration, pairs of noisy images of the same location and acquired in the same configuration (same incidence angle and mode as during the revisit of the satellite on its orbit). Our approach applies to a wide range of sensors. Experiments on RADARSAT-2 and RADARSAT Constellation Mission (RCM) data demonstrate the capacity of the proposed method to effectively reduce speckle noise and retrieve fine details. The code of the trained models is made freely available at <span><span>https://gitlab.telecom-paris.fr/ring/polsar2polsar</span><svg><path></path></svg></span> The repository additionally contains a model fine-tuned on SLC PolSAR images from NASA’s UAVSAR sensor.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 783-798"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scattering mechanism-guided zero-shot PolSAR target recognition","authors":"Feng Li ,&nbsp;Xiaojing Yang ,&nbsp;Liang Zhang ,&nbsp;Yanhua Wang ,&nbsp;Yuqi Han ,&nbsp;Xin Zhang ,&nbsp;Yang Li","doi":"10.1016/j.isprsjprs.2024.12.022","DOIUrl":"10.1016/j.isprsjprs.2024.12.022","url":null,"abstract":"<div><div>In response to the challenges posed by the difficulty in obtaining polarimetric synthetic aperture radar (PolSAR) data for certain specific categories of targets, we present a zero-shot target recognition method for PolSAR images. Based on a generative model, the method leverages the unique characteristics of polarimetric SAR images and incorporates two key modules: the scattering characteristics-guided semantic embedding generation module (SE) and the polarization characteristics-guided distributional correction module (DC). The former ensures the stability of synthetic features for unseen classes by controlling scattering characteristics. At the same time, the latter enhances the quality of synthetic features by utilizing polarimetric features, thereby improving the accuracy of zero-shot recognition. The proposed method is evaluated on the GOTCHA dataset to assess its performance in recognizing unseen classes. The experiment results demonstrate that the proposed method achieves SOTA performance in zero-shot PolSAR target recognition (<em>e.g.,</em> improving the recognition accuracy of unseen categories by nearly 20%). Our codes are available at <span><span>https://github.com/chuyihuan/Zero-shot-PolSAR-target-recognition</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 428-439"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of SAR-Optical fusion to extract shoreline position from Cloud-Contaminated satellite images","authors":"Yongjing Mao,&nbsp;Kristen D. Splinter","doi":"10.1016/j.isprsjprs.2025.01.013","DOIUrl":"10.1016/j.isprsjprs.2025.01.013","url":null,"abstract":"<div><div>Shorelines derived from optical satellite images are increasingly being used for regional to global scale analysis of sandy coastline dynamics. The optical satellite record, however, is contaminated by cloud cover, which can substantially reduce the temporal resolution of available images for shoreline analysis. Meanwhile, with the development of deep learning methods, optical images are increasingly fused with Synthetic Aperture Radar (SAR) images that are unaffected by clouds to reconstruct the cloud-contaminated pixels. Such SAR-Optical fusion methods have been shown successful for different land surface applications, but the unique characteristics of coastal areas make the applicability of this method unknown in these dynamic zones.</div><div>Herein we apply a deep internal learning (DIL) method to reconstruct cloud-contaminated optical images and explore its applicability to retrieve shorelines obscured by clouds. Our approach uses a mixed sequence of SAR and Gaussian noise images as the prior and the cloudy Modified Normalized Difference Water Index (MNDWI) as the target. The DIL encodes the target with priors and synthesizes plausible pixels under cloud cover. A unique aspect of our workflow is the inclusion of Gaussian noise in the prior sequence for MNDWI images when SAR images collected within a 1-day temporal lag are not available. A novel loss function of DIL model is also introduced to optimize the image reconstruction near the shoreline. These new developments have significant contribution to the model accuracy.</div><div>The DIL method is tested at four different sites with varying tide, wave, and shoreline dynamics. Shorelines derived from the reconstructed and true MNDWI images are compared to quantify the internal accuracy of shoreline reconstruction. For microtidal environments with mean springs tidal range less than 2 m, the mean absolute error (MAE) of shoreline reconstruction is less than 7.5 m with the coefficient of determination (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></mrow></math></span>) more than 0.78 regardless of shoreline and wave dynamics. The method is less skilful in macro- and mesotidal environments due to the larger water level difference in the paired optical and SAR images, resulting in the MAE of 12.59 m and <span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></mrow></math></span> of 0.43. The proposed SAR-Optical fusion method demonstrates substantially better accuracy in retrieving cloud-obscured shoreline positions compared to interpolation methods relying solely on optical images. Results from our work highlight the great potential of SAR-Optical fusion to derive shorelines even under the cloudiest conditions, thus increasing the temporal resolution of shoreline datasets.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 563-579"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refined change detection in heterogeneous low-resolution remote sensing images for disaster emergency response","authors":"Di Wang ,&nbsp;Guorui Ma ,&nbsp;Haiming Zhang ,&nbsp;Xiao Wang ,&nbsp;Yongxian Zhang","doi":"10.1016/j.isprsjprs.2024.12.010","DOIUrl":"10.1016/j.isprsjprs.2024.12.010","url":null,"abstract":"<div><div>Heterogeneous Remote Sensing Images Change Detection (HRSICD) is a significant challenge in remote sensing image processing, with substantial application value in rapid natural disaster response. However, significant differences in imaging modalities often result in poor comparability of their features, affecting the recognition accuracy. To address the issue, we propose a novel HRSICD method based on image structure relationships and semantic information. First, we employ a Multi-scale Pyramid Convolution Encoder to efficiently extract the multi-scale and detailed features. Next, the Cross-domain Feature Alignment Module aligns the structural relationships and semantic features of the heterogeneous images, enhancing the comparability between heterogeneous image features. Finally, the Multi-level Decoder fuses the structural and semantic features, achieving refined identification of change areas. We validated the advancement of proposed method on five publicly available HRSICD datasets. Additionally, zero-shot generalization experiments and real-world applications were conducted to assess its generalization capability. Our method achieved favorable results in all experiments, demonstrating its effectiveness. The code of the proposed method will be made available at <span><span>https://github.com/Lucky-DW/HRSICD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 139-155"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PylonModeler: A hybrid-driven 3D reconstruction method for power transmission pylons from LiDAR point clouds","authors":"Shaolong Wu ,&nbsp;Chi Chen ,&nbsp;Bisheng Yang ,&nbsp;Zhengfei Yan ,&nbsp;Zhiye Wang ,&nbsp;Shangzhe Sun ,&nbsp;Qin Zou ,&nbsp;Jing Fu","doi":"10.1016/j.isprsjprs.2024.12.003","DOIUrl":"10.1016/j.isprsjprs.2024.12.003","url":null,"abstract":"<div><div>As the power grid is an indispensable foundation of modern society, creating a digital twin of the grid is of great importance. Pylons serve as components in the transmission corridor, and their precise 3D reconstruction is essential for the safe operation of power grids. However, 3D pylon reconstruction from LiDAR point clouds presents numerous challenges due to data quality and the diversity and complexity of pylon structures. To address these challenges, we introduce PylonModeler: a hybrid-driven method for 3D pylon reconstruction using airborne LiDAR point clouds, thereby enabling accurate, robust, and efficient real-time pylon reconstruction. Different strategies are employed to achieve independent reconstructions and assemblies for various structures. We propose Pylon Former, a lightweight transformer network for real-time pylon recognition and decomposition. Subsequently, we apply a data-driven approach for the pylon body reconstruction. Considering structural characteristics, fitting and clustering algorithms are used to reconstruct both external and internal structures. The pylon head is reconstructed using a hybrid approach. A pre-built pylon head parameter model library defines different pylons by a series of parameters. The coherent point drift (CPD) algorithm is adopted to establish the topological relationships between pylon head structures and set initial model parameters, which are refined through optimization for accurate pylon head reconstruction. Finally, the pylon body and head models are combined to complete the reconstruction. We collected an airborne LiDAR dataset, which includes a total of 3398 pylon data across eight types. The dataset consists of transmission lines of various voltage levels, such as 110 kV, 220 kV, and 500 kV. PylonModeler is validated on this dataset. The average reconstruction time of a pylon is 1.10 s, with an average reconstruction accuracy of 0.216 m. In addition, we evaluate the performance of PylonModeler on public airborne LiDAR data from Luxembourg. Compared to previous state-of-the-art methods, reconstruction accuracy improved by approximately 26.28 %. With superior performance, PylonModeler is tens of times faster than the current model-driven methods, enabling real-time pylon reconstruction.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 100-124"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unwrapping error and fading signal correction on multi-looked InSAR data","authors":"Zhangfeng Ma ,&nbsp;Nanxin Wang ,&nbsp;Yingbao Yang ,&nbsp;Yosuke Aoki ,&nbsp;Shengji Wei","doi":"10.1016/j.isprsjprs.2024.12.006","DOIUrl":"10.1016/j.isprsjprs.2024.12.006","url":null,"abstract":"<div><div>Multi-looking, aimed at reducing data size and improving the signal-to-noise ratio, is indispensable for large-scale InSAR data processing. However, the resulting “Fading Signal” caused by multi-looking breaks the phase consistency among triplet interferograms and introduces bias into the estimated displacements. This inconsistency challenges the assumption that only unwrapping errors are involved in triplet phase closure. Therefore, untangling phase unwrapping errors and fading signals from triplet phase closure is critical to achieving more precise InSAR measurements. To address this challenge, we propose a new method that mitigates phase unwrapping errors and fading signals. This new method consists of two key steps. The first step is triplet phase closure-based stacking, which allows for the direct estimation of fading signals in each interferogram. The second step is Basis Pursuit Denoising-based unwrapping error correction, which transforms unwrapping error correction into sparse signal recovery. Through these two procedures, the new method can be seamlessly integrated into the traditional InSAR workflow. Additionally, the estimated fading signal can be directly used to derive soil moisture as a by-product of our method. Experimental results on the San Francisco Bay area demonstrate that the new method reduces velocity estimation errors by approximately 9 %–19 %, effectively addressing phase unwrapping errors and fading signals. This performance outperforms both ILP and Lasso methods, which only account for unwrapping errors in the triplet closure. Additionally, the derived by-product, soil moisture, shows strong consistency with most external soil moisture products.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 51-63"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PSO-based fine polarimetric decomposition for ship scattering characterization","authors":"Junpeng Wang ,&nbsp;Sinong Quan ,&nbsp;Shiqi Xing ,&nbsp;Yongzhen Li ,&nbsp;Hao Wu ,&nbsp;Weize Meng","doi":"10.1016/j.isprsjprs.2024.11.015","DOIUrl":"10.1016/j.isprsjprs.2024.11.015","url":null,"abstract":"<div><div>Due to the inappropriate estimation and inadequate awareness of scattering from complex substructures within ships, a reasonable, reliable, and complete interpretation tool to characterize ship scattering for polarimetric synthetic aperture radar (PolSAR) is still lacking. In this paper, a fine polarimetric decomposition with explicit physical meaning is proposed to reveal and characterize the local-structure-related scattering behaviors on ships. To this end, a nine-component decomposition scheme is first established through incorporating the rotated dihedral and planar resonator scattering models, which makes full use of polarimetric information and comprehensively considers the complex structure scattering of ships. In order to reasonably estimation the scattering components, three practical scattering dominance principles as well as an explicit objective function are raised, and a particle swarm optimization (PSO)-based model inversion strategy is subsequently presented. This not only overcomes the underdetermined problem, but also improves the scattering mechanism ambiguity by circumventing the constrained estimation order. Finally, a ship indicator by linearly combining the output scattering contribution is further derived, which constitutes a complete ship scattering interpretation approach along with the proposed decomposition. Experiments carried out with real PolSAR datasets demonstrate that the proposed method adequately and objectively describes the scatterers on ships, which provides an effective way to ship scattering characterization. Moreover, it also verifies the feasibility of fine polarimetric decomposition in a further application with the quantitative analysis of scattering components.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 18-31"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}