ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"A UAV-based sparse viewpoint planning framework for detailed 3D modelling of cultural heritage monuments","authors":"Zebiao Wu ,&nbsp;Patrick Marais ,&nbsp;Heinz Rüther","doi":"10.1016/j.isprsjprs.2024.10.028","DOIUrl":"10.1016/j.isprsjprs.2024.10.028","url":null,"abstract":"<div><div>Creating 3D digital models of heritage sites typically involves laser scanning and photogrammetry. Although laser scan-derived point clouds provide detailed geometry, occlusions and hidden areas often lead to gaps. Terrestrial and UAV photography can largely fill these gaps and also enhance definition and accuracy at edges and corners. Historical buildings with complex architectural or decorative details require a systematically planned combination of laser scanning with handheld and UAV photography. High-resolution photography not only enhances the geometry of 3D building models but also improves their texturing. The use of cameras, especially UAV cameras, requires robust viewpoint planning to ensure sufficient coverage of the documented structure whilst minimising viewpoints for efficient image acquisition and processing economy. Determining ideal viewpoints for detailed modelling is challenging. Existing planners, relying on coarse scene proxies, often miss fine structures, significantly restrict the search space of candidate viewpoints and surface targets due to high computational costs, and are sensitive to surface orientation errors, which limits their applicability in complex scenarios. To address these limitations, we propose a strategy for generating sparse viewpoints from point clouds for efficient and accurate UAV-based modelling. Unlike existing planners, our backward visibility approach enables exploration of the camera viewpoint space at low computational cost and does not require surface orientation (normal vector) estimation. We introduce an observability-based planning criterion, a direction diversity-driven reconstructability criterion, which assesses modelling quality by encouraging global diversity in viewing directions, and a coarse-to-fine adaptive viewpoint search approach that builds on these criteria. The approach was validated on a number of complex heritage scenes. It achieves efficient modelling with minimal viewpoints and accurately captures fine structures, like thin spires, that are problematic for other planners. For our test examples, we achieve at least 98% coverage, using significantly fewer viewpoints, and with a consistently high structural similarity across all models.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 555-571"},"PeriodicalIF":10.6,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722525","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":"Optimizing hybrid models for canopy nitrogen mapping from Sentinel-2 in Google Earth Engine","authors":"Emma De Clerck ,&nbsp;Dávid D.Kovács ,&nbsp;Katja Berger ,&nbsp;Martin Schlerf ,&nbsp;Jochem Verrelst","doi":"10.1016/j.isprsjprs.2024.11.005","DOIUrl":"10.1016/j.isprsjprs.2024.11.005","url":null,"abstract":"<div><div>Canopy nitrogen content (CNC) is a crucial variable for plant health, influencing photosynthesis and growth. An optimized, scalable approach for spatially explicit CNC quantification using Sentinel-2 (S2) data is presented, integrating PROSAIL-PRO simulations with Gaussian Process Regression (GPR) and an Active Learning technique, specifically the Euclidean distance-based diversity (EBD) approach for selective sampling. This hybrid method enhances training dataset efficiency and optimizes CNC models for practical applications. Two GPR models based on PROSAIL-PRO variables were evaluated: a protein-based model (C<span><math><msub><mrow></mrow><mrow><mtext>prot</mtext></mrow></msub></math></span>-LAI) and a chlorophyll-based model (C<span><math><msub><mrow></mrow><mrow><mtext>ab</mtext></mrow></msub></math></span>-LAI). Both models, implemented in Google Earth Engine (GEE), demonstrated strong performance and outperformed other machine learning methods, including kernel ridge regression, principal component regression, neural network, weighted k-nearest neighbors regression, partial least squares regression and least squares linear regression. Validation results showed moderate to good accuracies: NRMSE<span><math><msub><mrow></mrow><mrow><msub><mrow><mi>C</mi></mrow><mrow><mtext>prot</mtext><mo>−</mo><mtext>LAI</mtext></mrow></msub></mrow></msub></math></span> = 16.76%, R<span><math><msubsup><mrow></mrow><mrow><msub><mrow><mi>C</mi></mrow><mrow><mtext>prot</mtext><mo>−</mo><mtext>LAI</mtext></mrow></msub></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> = 0.47; NRMSE<span><math><msub><mrow></mrow><mrow><msub><mrow><mi>C</mi></mrow><mrow><mtext>ab</mtext><mo>−</mo><mtext>LAI</mtext></mrow></msub></mrow></msub></math></span> = 18.74%, R<span><math><msubsup><mrow></mrow><mrow><msub><mrow><mi>C</mi></mrow><mrow><mtext>ab</mtext><mo>−</mo><mtext>LAI</mtext></mrow></msub></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> = 0.51. The models revealed high consistency for an independent validation dataset of the Munich-North-Isar (Germany) test site, with R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> values of 0.58 and 0.71 and NRMSEs of 21.47% and 20.17% for the C<span><math><msub><mrow></mrow><mrow><mtext>prot</mtext></mrow></msub></math></span>-LAI model and C<span><math><msub><mrow></mrow><mrow><mtext>ab</mtext></mrow></msub></math></span>-LAI model, respectively. The models also demonstrated high consistency across growing seasons, indicating their potential for time series analysis of CNC dynamics. Application of the S2-based mapping workflow across the Iberian Peninsula, with estimates showing relative uncertainty below 30%, highlights the model’s broad applicability and portability. The optimized EBD-GPR-CNC approach within GEE supports scalable CNC estimation and offers a robust tool for monitoring nitrogen dynamics.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 530-545"},"PeriodicalIF":10.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691119","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":"A unique dielectric constant estimation for lunar surface through PolSAR model-based decomposition","authors":"Inderkumar Kochar ,&nbsp;Anup Das ,&nbsp;Rajib Kumar Panigrahi","doi":"10.1016/j.isprsjprs.2024.10.022","DOIUrl":"10.1016/j.isprsjprs.2024.10.022","url":null,"abstract":"<div><div>Dielectric constant for the earth and planetary surfaces has been estimated using reflection coefficients in the past. A recent trend is to use model-based decomposition for dielectric constant retrieval from polarimetric synthetic aperture radar (polSAR) data. We examine the reported literature in this regard and propose a unique dielectric constant estimation (UDCE) algorithm using three-component decomposition technique. In UDCE, the dielectric constant is obtained directly from one of the elements of the measured coherency matrix in a single step. The dielectric constant estimate from the UDCE is independent of the volume scattering model when single-bounce or double-bounce scattering is dominant. This avoids error propagation from overestimation of volume scattering to the copolarization ratios, and in turn, to the dielectric constant, inherent in reported algorithms that use model-based decomposition. Consequently, a unique solution is obtained. We also demonstrate that the solution from the UDCE is unaffected by using a higher-order model-based decomposition. We evaluate the performance of the proposed UDCE algorithm over three Apollo 12, Apollo 15, and Apollo 17 landing sites on the lunar surface using Chandrayaan- 2 dual-frequency synthetic aperture radar (DFSAR) datasets. An excellent convergence rate for dielectric constant estimation is maintained over all three test sites. Using the proposed UDCE algorithm, the dielectric constant maps are produced for the lunar surface using full polSAR data for the first time. We observe that the generated dielectric constant maps capture all the ground truth features, previously unseen with such clarity.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 546-554"},"PeriodicalIF":10.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691120","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":"Unwrap-Net: A deep neural network-based InSAR phase unwrapping method assisted by airborne LiDAR data","authors":"Wang Yang ,&nbsp;Yi He ,&nbsp;Qing Zhu ,&nbsp;Lifeng Zhang ,&nbsp;Long Jin","doi":"10.1016/j.isprsjprs.2024.11.009","DOIUrl":"10.1016/j.isprsjprs.2024.11.009","url":null,"abstract":"<div><div>In Interferometric Synthetic Aperture Radar (InSAR) data processing, accurately unwrapping the phase is crucial for measuring elevation or deformation. DCNN models such as PhaseNet and PGNet have improved the efficiency and accuracy of phase unwrapping, but they still face challenges such as incomplete multiscale feature learning, high feature redundancy, and reliance on unrealistic datasets. These limitations compromise their effectiveness in areas with low coherence and high gradient deformation. This study proposed Unwrap-Net, a novel network model featuring an encoder-decoder structure and enhanced multiscale feature learning via ASPP (Atrous Spatial Pyramid Pooling). Unwrap-Net minimizes feature redundancy and boosts learning efficiency using SERB (Residual Convolutional with SE-block). For dataset construction, airborne LiDAR terrain data combined with land cover data from optical images, using the SGS (Sequential Gaussian Simulation) method, are used to synthesize phase data and simulate decorrelation noise. This approach creates a dataset that closely approximates real-world conditions. Additionally, the introduction of a new high-fidelity optimization loss function significantly enhances the model’s resistance to noise. Experimental results show that compared to the SNAPHU and PhaseNet models, the SSIM of the Unwrap-Net model improves by over 13%, and the RMSE is reduced by more than 34% in simulated data experiments. In real data experiments, SSIM improves by over 6%, and RMSE is reduced by more than 49%. This indicates that the unwrapping results of the Unwrap-Net model are more reliable and have stronger generalization capabilities. The related experimental code and dataset will be made available at <span><span>https://github.com/yangwangyangzi48/UNWRAPNETV1.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 510-529"},"PeriodicalIF":10.6,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691121","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":"METNet: A mesh exploring approach for segmenting 3D textured urban scenes","authors":"Qendrim Schreiber ,&nbsp;Nicola Wolpert ,&nbsp;Elmar Schömer","doi":"10.1016/j.isprsjprs.2024.10.020","DOIUrl":"10.1016/j.isprsjprs.2024.10.020","url":null,"abstract":"<div><div>In this work, we present the neural network Mesh Exploring Tensor Net (METNet) for the segmentation of 3D urban scenes, that operates directly on textured meshes. Since triangular meshes have a very irregular structure, many existing approaches change the input by sampling evenly distributed point clouds on the meshes. The resulting simplified representation of the urban scenes has the advantage that state-of-the-art neural network architectures for point clouds can be used for the segmentation task. The disadvantages are that crucial geodesic information is lost and for a sufficiently good approximation of the scene many points are often necessary. Since memory on the GPU is limited, the consequence is that only small areas can be examined locally.</div><div>To overcome these limitations, METNet generates its input directly from the textured triangular mesh. It applies a new star-shaped exploration strategy, starting from a triangle on the mesh and expanding in various directions. This way, a vertex based regular local tensor from the unstructured mesh is generated, which we call a Mesh Exploring Tensor (MET). By expanding on the mesh, a MET maintains the information about the connectivity and distance of vertices along the surface of the mesh. It also effectively captures the characteristics of large regions. Our new architecture, METNet is optimized for processing METs as input. The regular structure of the input allows METNet the use of established convolutional neural networks.</div><div>Experimental results, conducted on two urban textured mesh benchmarks, demonstrate that METNet surpasses the performance of previous state-of-the-art techniques. METNet improves the previous state-of-the-art results by 8.6% in terms of mean IoU (intersection over union) on the SUM dataset compared to the second-best method PSSNet, and by 3.2% in terms of mean F1 score on the H3D dataset compared to the second-best method ifp-SCN. Our source code is available at <span><span>https://github.com/QenSchr/METNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 498-509"},"PeriodicalIF":10.6,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691123","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":"On-orbit geometric calibration of MERSI whiskbroom scanner","authors":"Hongbo Pan,&nbsp;Xue Zhang,&nbsp;Zixuan Liu,&nbsp;Tao Huang","doi":"10.1016/j.isprsjprs.2024.11.007","DOIUrl":"10.1016/j.isprsjprs.2024.11.007","url":null,"abstract":"<div><div>The whiskbroom scanner is a critical component in remote sensing payloads, such as the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) and the Medium Resolution Spectral Imager (MERSI) on FengYun-3. However, panoramic distortion in whiskbroom scanner images increases overlap from the nadir to the edges between adjacent scans. These distortions present significant challenges for improving geolocation accuracy, particularly when errors occur in sensors and platforms. This manuscript derives analytic expressions for all potential error sources, including sensors, platforms, and elevation, using homogeneous coordinates in the focal plane. This derivation demonstrates that geolocation errors vary with view angles and detector positions. To further investigate these error properties, a gradient-aware least-squares matching method was developed to extract highly accurate and dense ground control points (GCPs) with approximately 100,000 points in a single scene. A three-step geometric calibration method was then introduced, which includes boresight misalignment correction, parametric geometric calibration, and non-uniform scanning compensation. Given the varying spatial resolution of the GCPs, the weight of the GCPs was dynamically updated for least-squares estimation. This method effectively demonstrated the complex geolocation errors in MERSI on FY-3D, a system that was not meticulously calibrated in the laboratory. The initial root mean square errors (RMSEs) were 3.354 and 12.441 instantaneous field of view (IFoV) for the designed parameters. The proposed geometric calibration method successfully corrected view-angle and detector position-related geolocation errors, reducing them to 0.211 and 0.225 IFoV in the scan and track directions, respectively. The geolocation validation software and experiment results were provided <span><span>https://github.com/hongbop/whiskgeovalidation.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 481-497"},"PeriodicalIF":10.6,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691122","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":"ACMatch: Improving context capture for two-view correspondence learning via adaptive convolution","authors":"Xiang Fang ,&nbsp;Yifan Lu ,&nbsp;Shihua Zhang ,&nbsp;Yining Xie ,&nbsp;Jiayi Ma","doi":"10.1016/j.isprsjprs.2024.11.004","DOIUrl":"10.1016/j.isprsjprs.2024.11.004","url":null,"abstract":"<div><div>Two-view correspondence learning plays a pivotal role in the field of computer vision. However, this task is beset with great challenges stemming from the significant imbalance between true and false correspondences. Recent approaches have started leveraging the inherent filtering properties of convolution to eliminate false matches. Nevertheless, these methods tend to apply convolution in an ad hoc manner without careful design, thereby inheriting the limitations of convolution and hindering performance improvement. In this paper, we propose a novel convolution-based method called ACMatch, which aims to meticulously design convolutional filters to mitigate the shortcomings of convolution and enhance its effectiveness. Specifically, to address the limitation of existing convolutional filters of struggling to effectively capture global information due to the limited receptive field, we introduce a strategy to help them obtain relatively global information by guiding grid points to incorporate more contextual information, thus enabling a global perspective for two-view learning. Furthermore, we recognize that in the context of feature matching, inliers and outliers provide fundamentally different information. Hence, we design an adaptive weighted convolution module that allows the filters to focus more on inliers while ignoring outliers. Extensive experiments across various visual tasks demonstrate the effectiveness, superiority, and generalization. Notably, ACMatch attains an AUC@5° of 35.93% on YFCC100M without RANSAC, surpassing the previous state-of-the-art by 5.85 absolute percentage points and exceeding the 35% AUC@5° bar for the first time. Our code is publicly available at <span><span>https://github.com/ShineFox/ACMatch</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 466-480"},"PeriodicalIF":10.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658279","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":"A universal adapter in segmentation models for transferable landslide mapping","authors":"Ruilong Wei ,&nbsp;Yamei Li ,&nbsp;Yao Li ,&nbsp;Bo Zhang ,&nbsp;Jiao Wang ,&nbsp;Chunhao Wu ,&nbsp;Shunyu Yao ,&nbsp;Chengming Ye","doi":"10.1016/j.isprsjprs.2024.11.006","DOIUrl":"10.1016/j.isprsjprs.2024.11.006","url":null,"abstract":"<div><div>Efficient landslide mapping is crucial for disaster mitigation and relief. Recently, deep learning methods have shown promising results in landslide mapping using satellite imagery. However, the sample sparsity and geographic diversity of landslides have challenged the transferability of deep learning models. In this paper, we proposed a universal adapter module that can be seamlessly embedded into existing segmentation models for transferable landslide mapping. The adapter can achieve high-accuracy cross-regional landslide segmentation with a small sample set, requiring minimal parameter adjustments. In detail, the pre-trained baseline model freezes its parameters to keep learned knowledge of the source domain, while the lightweight adapter fine-tunes only a few parameters to learn new landslide features of the target domain. Structurally, we introduced an attention mechanism to enhance the feature extraction of the adapter. To validate the proposed adapter module, 4321 landslide samples were prepared, and the Segment Anything Model (SAM) and other baseline models, along with four transfer strategies were selected for controlled experiments. In addition, Sentinel-2 satellite imagery in the Himalayas and Hengduan Mountains, located on the southern and southeastern edges of the Tibetan Plateau was collected for evaluation. The controlled experiments reported that SAM, when combined with our adapter module, achieved a peak mean Intersection over Union (mIoU) of 82.3 %. For other baseline models, integrating the adapter improved mIoU by 2.6 % to 12.9 % compared with traditional strategies on cross-regional landslide mapping. In particular, baseline models with Transformers are more suitable for fine-tuning parameters. Furthermore, the visualized feature maps revealed that fine-tuning shallow encoders can achieve better effects in model transfer. Besides, the proposed adapter can effectively extract landslide features and focus on specific spatial and channel domains with significant features. We also quantified the spectral, scale, and shape features of landslides and analyzed their impacts on segmentation results. Our analysis indicated that weak spectral differences, as well as extreme scale and edge shapes are detrimental to the accuracy of landslide segmentation. Overall, this adapter module provides a new perspective for large-scale transferable landslide mapping.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 446-465"},"PeriodicalIF":10.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658278","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":"Contrastive learning for real SAR image despeckling","authors":"Yangtian Fang ,&nbsp;Rui Liu ,&nbsp;Yini Peng ,&nbsp;Jianjun Guan ,&nbsp;Duidui Li ,&nbsp;Xin Tian","doi":"10.1016/j.isprsjprs.2024.11.003","DOIUrl":"10.1016/j.isprsjprs.2024.11.003","url":null,"abstract":"<div><div>The use of synthetic aperture radar (SAR) has greatly improved our ability to capture high-resolution terrestrial images under various weather conditions. However, SAR imagery is affected by speckle noise, which distorts image details and hampers subsequent applications. Recent forays into supervised deep learning-based denoising methods, like MRDDANet and SAR-CAM, offer a promising avenue for SAR despeckling. However, they are impeded by the domain gaps between synthetic data and realistic SAR images. To tackle this problem, we introduce a self-supervised speckle-aware network to utilize the limited near-real datasets and unlimited synthetic datasets simultaneously, which boosts the performance of the downstream despeckling module by teaching the module to discriminate the domain gap of different datasets in the embedding space. Specifically, based on contrastive learning, the speckle-aware network first characterizes the discriminative representations of spatial-correlated speckle noise in different images across diverse datasets, which provides priors of versatile speckles and image characteristics. Then, the representations are effectively modulated into a subsequent multi-scale despeckling network to generate authentic despeckled images. In this way, the despeckling module can reconstruct reliable SAR image characteristics by learning from near-real datasets, while the generalization performance is guaranteed by learning abundant patterns from synthetic datasets simultaneously. Additionally, a novel excitation aggregation pooling module is inserted into the despeckling network to enhance the network further, which utilizes features from different levels of scales and better preserves spatial details around strong scatters in real SAR images. Extensive experiments across real SAR datasets from Sentinel-1, Capella-X, and TerraSAR-X satellites are carried out to verify the effectiveness of the proposed method over other state-of-the-art methods. Specifically, the proposed method achieves the best PSNR and SSIM values evaluated on the near-real Sentinel-1 dataset, with gains of 0.22 dB in PSNR compared to MRDDANet, and improvements of 1.3% in SSIM over SAR-CAM. The code is available at <span><span>https://github.com/YangtianFang2002/CL-SAR-Despeckling</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 376-391"},"PeriodicalIF":10.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658338","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":"MIWC: A multi-temporal image weighted composition method for satellite-derived bathymetry in shallow waters","authors":"Zhixin Duan ,&nbsp;Liang Cheng ,&nbsp;Qingzhou Mao ,&nbsp;Yueting Song ,&nbsp;Xiao Zhou ,&nbsp;Manchun Li ,&nbsp;Jianya Gong","doi":"10.1016/j.isprsjprs.2024.10.009","DOIUrl":"10.1016/j.isprsjprs.2024.10.009","url":null,"abstract":"<div><div>Satellite-derived bathymetry (SDB) is a vital technique for the rapid and cost-effective measurement of shallow underwater terrain. However, it faces challenges of image noise, including clouds, bubble clouds, and sun glint. Consequently, the acquisition of no missing and accurate bathymetric maps is frequently challenging, particularly in cloudy, rainy, and large-scale regions. In this study, we propose a multi-temporal image weighted composition (MIWC) method. This method performs iterative segmentation and inverse distance weighted composition of multi-temporal images based only on the near-infrared (NIR) band information of multispectral images to obtain high-quality composite images. The method was applied to scenarios using Sentinel-2 imagery for bathymetry of four representative areas located in the South China Sea and the Atlantic Ocean. The results show that the root mean square error (RMSE) of bathymetry from the composite images using the log-transformed linear model (LLM) and the log-transformed ratio model (LRM) in the water depth range of 0–20 m are 0.67–1.22 m and 0.71–1.23 m, respectively. The RMSE of the bathymetry decreases with the number of images involved in the composition and tends to be relatively stable when the number of images reaches approximately 16. In addition, the composition images generated by the MIWC method generally exhibit not only superior visual quality, but also significant advantages in terms of bathymetric accuracy and robustness when compared to the best single images as well as the composition images generated by the median composition method and the maximum outlier removal method. The recommended value of the power parameter for inverse distance weighting in the MIWC method was experimentally determined to be 4, which typically does not require complex adjustments, making the method easy to apply or integrate. The MIWC method offers a reliable approach to improve the quality of remote sensing images, ensuring the completeness and accuracy of shallow water bathymetric maps.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"218 ","pages":"Pages 430-445"},"PeriodicalIF":10.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658277","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}