ISPRS Journal of Photogrammetry and Remote Sensing最新文献

{"title":"Remote sensing scene graph generation for improved retrieval based on spatial relationships","authors":"Jiayi Tang,&nbsp;Xiaochong Tong,&nbsp;Chunping Qiu,&nbsp;Yuekun Sun,&nbsp;Haoshuai Song,&nbsp;Yaxian Lei,&nbsp;Yi Lei,&nbsp;Congzhou Guo","doi":"10.1016/j.isprsjprs.2025.01.012","DOIUrl":"10.1016/j.isprsjprs.2025.01.012","url":null,"abstract":"<div><div>RS scene graphs represent RS scenes as graphs with objects as nodes and their spatial relationships as edges, playing a crucial role in understanding and interpreting RS scenes at a higher level. However, existing RS scene graph generation methods, relying on deep learning models, face limitations due to their dependence on extensive relationship labels, restricted generation accuracy, and limited generalizability. To address these challenges, we proposed a spatial relationship computing model based on prior geographic information knowledge for RS scene graph generation. We refer to the RS scene graph generated using our method as SG-SSR for short. Furthermore, we investigated the application of SG-SSR in RS scene retrieval, demonstrating improved retrieval accuracy for spatial relationships between entities. The experiments show that our scene graph generation method does not rely on relationship labels, and has higher generation accuracy and greater universality. Moreover, the retrieval method based on SG-SSR outperformed other retrieval methods based on image feature vectors, with a retrieval accuracy index 0.098 higher than the alternatives(RemoteCLIP(mask)). The dataset and code are available at <span><span>https://gitee.com/tangjiayitangjiayi/sg-ssr</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 741-752"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072525","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":"Synthesis of complex-valued InSAR data with a multi-task convolutional neural network","authors":"Philipp Sibler ,&nbsp;Francescopaolo Sica ,&nbsp;Michael Schmitt","doi":"10.1016/j.isprsjprs.2024.12.007","DOIUrl":"10.1016/j.isprsjprs.2024.12.007","url":null,"abstract":"<div><div>Simulated remote sensing images bear great potential for many applications in the field of Earth observation. They can be used as controlled testbed for the development of signal and image processing algorithms or can provide a means to get an impression of the potential of new sensor concepts. With the rise of deep learning, the synthesis of artificial remote sensing images by means of deep neural networks has become a hot research topic. While the generation of optical data is relatively straightforward, as it can rely on the use of established models from the computer vision community, the generation of synthetic aperture radar (SAR) data until now is still largely restricted to intensity images since the processing of complex-valued numbers by conventional neural networks poses significant challenges. With this work, we propose to circumvent these challenges by decomposing SAR interferograms into real-valued components. These components are then simultaneously synthesized by different branches of a multi-branch encoder–decoder network architecture. In the end, these real-valued components can be combined again into the final, complex-valued interferogram. Moreover, the effect of speckle and interferometric phase noise is replicated and applied to the synthesized interferometric data. Experimental results on both medium-resolution C-band repeat-pass SAR data and high-resolution X-band single-pass SAR data, demonstrate the general feasibility of the approach.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 192-206"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874573","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":"Large-scale rice mapping under spatiotemporal heterogeneity using multi-temporal SAR images and explainable deep learning","authors":"Ji Ge ,&nbsp;Hong Zhang ,&nbsp;Lijun Zuo ,&nbsp;Lu Xu ,&nbsp;Jingling Jiang ,&nbsp;Mingyang Song ,&nbsp;Yinhaibin Ding ,&nbsp;Yazhe Xie ,&nbsp;Fan Wu ,&nbsp;Chao Wang ,&nbsp;Wenjiang Huang","doi":"10.1016/j.isprsjprs.2024.12.021","DOIUrl":"10.1016/j.isprsjprs.2024.12.021","url":null,"abstract":"<div><div>Timely and accurate mapping of rice cultivation distribution is crucial for ensuring global food security and achieving SDG2. From a global perspective, rice areas display high heterogeneity in spatial pattern and SAR time-series characteristics, posing substantial challenges to deep learning (DL) models’ performance, efficiency, and transferability. Moreover, due to their “black box” nature, DL often lack interpretability and credibility. To address these challenges, this paper constructs the first SAR rice dataset with spatiotemporal heterogeneity and proposes an explainable, lightweight model for rice area extraction, the eXplainable Mamba UNet (XM-UNet). The dataset is based on the 2023 multi-temporal Sentinel-1 data, covering diverse rice samples from the United States, Kenya, and Vietnam. A Temporal Feature Importance Explainer (TFI-Explainer) based on the Selective State Space Model is designed to enhance adaptability to the temporal heterogeneity of rice and the model’s interpretability. This explainer, coupled with the DL model, provides interpretations of the importance of SAR temporal features and facilitates crucial time phase screening. To overcome the spatial heterogeneity of rice, an Attention Sandglass Layer (ASL) combining CNN and self-attention mechanisms is designed to enhance the local spatial feature extraction capabilities. Additionally, the Parallel Visual State Space Layer (PVSSL) utilizes 2D-Selective-Scan (SS2D) cross-scanning to capture the global spatial features of rice multi-directionally, significantly reducing computational complexity through parallelization. Experimental results demonstrate that the XM-UNet adapts well to the spatiotemporal heterogeneity of rice globally, with OA and F1-score of 94.26 % and 90.73 %, respectively. The model is extremely lightweight, with only 0.190 M parameters and 0.279 GFLOPs. Mamba’s selective scanning facilitates feature screening, and its integration with CNN effectively balances rice’s local and global spatial characteristics. The interpretability experiments prove that the explanations of the importance of the temporal features provided by the model are crucial for guiding rice distribution mapping and filling a gap in the related field. The code is available in <span><span>https://github.com/SAR-RICE/XM-UNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 395-412"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925270","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":"Multiscale adaptive PolSAR image superpixel generation based on local iterative clustering and polarimetric scattering features","authors":"Nengcai Li ,&nbsp;Deliang Xiang ,&nbsp;Xiaokun Sun ,&nbsp;Canbin Hu ,&nbsp;Yi Su","doi":"10.1016/j.isprsjprs.2024.12.011","DOIUrl":"10.1016/j.isprsjprs.2024.12.011","url":null,"abstract":"<div><div>Superpixel generation is an essential preprocessing step for intelligent interpretation of object-level Polarimetric Synthetic Aperture Radar (PolSAR) images. The Simple Linear Iterative Clustering (SLIC) algorithm has become one of the primary methods for superpixel generation in PolSAR images due to its advantages of minimal human intervention and ease of implementation. However, existing SLIC-based superpixel generation methods for PolSAR images often use distance measures based on the complex Wishart distribution as the similarity metric. These methods are not ideal for segmenting heterogeneous regions, and a single superpixel generation result cannot simultaneously extract coarse and fine levels of detail in the image. To address this, this paper proposes a multiscale adaptive superpixel generation method for PolSAR images based on SLIC. To tackle the issue of the complex Wishart distribution’s inaccuracy in modeling urban heterogeneous regions, this paper employs the polarimetric target decomposition method. It extracts the polarimetric scattering features of the land cover, then constructs a similarity measure for these features using Riemannian metric. To achieve multiscale superpixel segmentation in a single superpixel segmentation process, this paper introduces a new method for initializing cluster centers based on polarimetric homogeneity measure. This initialization method assigns denser cluster centers in heterogeneous areas and automatically adjusts the size of the search regions according to the polarimetric homogeneity measure. Finally, a novel clustering distance metric is defined, integrating multiple types of information, including polarimetric scattering feature similarity, power feature similarity, and spatial similarity. This metric uses the polarimetric homogeneity measure to adaptively balance the relative weights between the various similarities. Comparative experiments were conducted using three real PolSAR datasets with state-of-the-art SLIC-based methods (Qin-RW and Yin-HLT). The results demonstrate that the proposed method provides richer multiscale detail information and significantly improves segmentation outcomes. For example, with the AIRSAR dataset and the step size of 42, the proposed method achieves improvements of 16.56<span><math><mtext>%</mtext></math></span> in BR and 12.01<span><math><mtext>%</mtext></math></span> in ASA compared to the Qin-RW method. Source code of the proposed method is made available at <span><span>https://github.com/linengcai/PolSAR_MS_ASLIC.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 307-322"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889391","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":"Accurate and complete neural implicit surface reconstruction in street scenes using images and LiDAR point clouds","authors":"Chenhui Shi ,&nbsp;Fulin Tang ,&nbsp;Yihong Wu ,&nbsp;Hongtu Ji ,&nbsp;Hongjie Duan","doi":"10.1016/j.isprsjprs.2024.12.012","DOIUrl":"10.1016/j.isprsjprs.2024.12.012","url":null,"abstract":"<div><div>Surface reconstruction in street scenes is a critical task in computer vision and photogrammetry, with images and LiDAR point clouds being commonly used data sources. However, image-only reconstruction faces challenges such as lighting variations, weak textures, and sparse viewpoints, while LiDAR-only methods suffer from issues like sparse and noisy LiDAR point clouds. Effectively integrating these two modalities to leverage their complementary strengths remains an open problem. Inspired by recent advances in neural implicit representations, we propose a novel street-level neural implicit surface reconstruction approach that incorporates images and LiDAR point clouds into a unified framework for joint optimization. Three key components make our approach achieve state-of-the-art (SOTA) reconstruction performance with high accuracy and completeness in street scenes. First, we introduce an adaptive photometric constraint weighting method to mitigate the impacts of lighting variations and weak textures on reconstruction. Second, a new B-spline-based hierarchical hash encoder is proposed to ensure the continuity of gradient-derived normals and further to reduce the noise from images and LiDAR point clouds. Third, we implement effective signed distance field (SDF) constraints in a spatial hash grid allocated in near-surface space to fully exploit the geometric information provided by LiDAR point clouds. Additionally, we present two street-level datasets—one virtual and one real-world—offering a comprehensive set of resources that existing public datasets lack. Experimental results demonstrate the superior performance of our method. Compared to the SOTA image-LiDAR combined neural implicit method, namely StreetSurf, ours significantly improves the F-score by approximately 7 percentage points. Our code and data are available at <span><span>https://github.com/SCH1001/StreetRecon</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 295-306"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889392","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":"KIBS: 3D detection of planar roof sections from a single satellite image","authors":"Johann Lussange ,&nbsp;Mulin Yu ,&nbsp;Yuliya Tarabalka ,&nbsp;Florent Lafarge","doi":"10.1016/j.isprsjprs.2024.11.014","DOIUrl":"10.1016/j.isprsjprs.2024.11.014","url":null,"abstract":"<div><div>Reconstructing urban areas in 3D from satellite raster images has been a long-standing problem for both academical and industrial research. While automatic methods achieving this objective at a Level Of Detail (LOD) 1 are mostly efficient today, producing LOD2 models is still a scientific challenge. In particular, the quality and resolution of satellite data is too low to infer accurately the planar roof sections in 3D by using traditional plane detection algorithms. Existing methods rely upon the exploitation of both strong urban priors that reduce their applicability to a variety of environments and multi-modal data, including some derived 3D products such as Digital Surface Models. In this work, we address this issue with KIBS (<em>Keypoints Inference By Segmentation</em>), a method that detects planar roof sections in 3D from a single-view satellite image. By exploiting large-scale LOD2 databases produced by human operators with efficient neural architectures, we manage to both segment roof sections in images and extract keypoints enclosing these sections in 3D to form 3D-polygons with a low-complexity. The output set of 3D-polygons can be used to reconstruct LOD2 models of buildings when combined with a plane assembly method. While conceptually simple, our method manages to capture roof sections as 3D-polygons with a good accuracy, from a single satellite image only by learning indirect 3D information contained in the image, in particular from the view inclination, the distortion of facades, the building shadows, roof peak and ridge perspective. We demonstrate the potential of KIBS by reconstructing large urban areas in a few minutes, with a Jaccard index for the 2D segmentation of individual roof sections of approximately 80%, and an altimetric error of the reconstructed LOD2 model of less than to 2 meters.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 207-216"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874579","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":"Using hyperspectral and thermal imagery to monitor stress of Southern California plant species during the 2013–2015 drought","authors":"Susan K. Meerdink ,&nbsp;Dar A. Roberts ,&nbsp;Jennifer Y. King ,&nbsp;Keely L. Roth ,&nbsp;Paul D. Gader ,&nbsp;Kelly K. Caylor","doi":"10.1016/j.isprsjprs.2025.01.015","DOIUrl":"10.1016/j.isprsjprs.2025.01.015","url":null,"abstract":"<div><div>From 2012 to 2015, California experienced the most severe drought since 1895, causing natural vegetation throughout the state to become water-stressed. With many areas in California being inaccessible and having extremely rugged terrain, remote sensing provides a means for monitoring plant stress across a broad landscape. Airborne hyperspectral and thermal imaging captured the drought in the spring, summer, and fall seasons of 2013 – 2015 across 11,640 km² of Southern California. Here we provide a large-scale analysis of plant species’ annual and seasonal temperature variability throughout this prolonged drought. We calculated the Temperature Condition Index (TCI) using airborne thermal imagery and a plant species classification map derived from airborne hyperspectral imagery to track response in three dominant species (e.g., Mediterranean grasses and forbs, chamise, and coast live oak) that have different stress adaptation strategies. The annual grasses and forbs showed strong seasonal changes in TCI, which corresponded to the typical green-up, peak biomass in summer, and senescence in the fall. They also had the strongest change in TCI values as the drought progressed from 2013 to 2015, with the months of April and August showing the most pronounced changes. The deeper rooted, native chamise evergreen shrub and coast live oak evergreen, broadleaf tree showed a more minor shift in seasonal and yearly patterns of TCI, but even these very well adapted species showed an increased amount of TCI stress as the drought progressed from 2013 to 2015. Across the study area and image dates, TCI stress was not evenly distributed, and in August 2015 almost the entire region experienced elevated TCI stress. To better understand the environment’s effect on plant stress, we relate topographic attributes to plant stress. Higher TCI values correlated with south or south-southwest facing slopes, while other topographic attributes were weakly correlated with TCI. An increase in elevation had a strong correlation with a decrease in TCI stress, but this relationship weakened as the drought progressed. The synergistic capabilities of hyperspectral and thermal imagery demonstrate that we can monitor the dynamic nature of plant species’ stress temporally and spatially. This work supports improved monitoring of natural landscapes and informing management possibilities, especially for areas prone to continued drought and high risk of wildfires.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 580-592"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989651","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":"GITomo-Net: Geometry-independent deep learning imaging method for SAR tomography","authors":"Changhao Liu ,&nbsp;Yan Wang ,&nbsp;Guangbin Zhang ,&nbsp;Zegang Ding ,&nbsp;Tao Zeng","doi":"10.1016/j.isprsjprs.2025.01.004","DOIUrl":"10.1016/j.isprsjprs.2025.01.004","url":null,"abstract":"<div><div>The utilization of deep learning in Tomographic SAR (TomoSAR) three-dimensional (3D) imaging technology addresses the inefficiency inherent in traditional compressed Sensing (CS)-based TomoSAR algorithms. However, current deep learning TomoSAR imaging methods heavily depend on prior knowledge of observation geometries, as the network training requires a predefined observation prior distribution. Additionally, discrepancies often exist between actual and designed observations in a TomoSAR task, making it challenging to train imaging networks before the task begins. Therefore, the current TomoSAR imaging networks suffer from high costs and lack universality. This paper introduces a new geometry-independent deep learning-based method for TomoSAR without the necessity of geometry as prior information, forming an adaptability to different observation geometries. First, a novel geometry-independent deep learning imaging model is introduced to adapt TomoSAR imaging tasks with unknown observation geometries by consolidating the data features of multiple geometries. Second, a geometry-independent TomoSAR imaging network (GITomo-Net) is proposed to adapt the new geometry-independent deep learning imaging model by introducing a transformation-feature normalization (TFN) module and a fully connected-based feature extraction (FCFE) layer, enabling the network to be capable of handling multi-geometries tasks. The proposed method has been validated using real spaceborne SAR data experiments. The average gradient (AG) and image entropy (IE) metrics for the Regent Beijing Hotel region are 7.11 and 2.85, respectively, while those for the COFCO Plaza region are 3.90 and 1.73, respectively. Compared to the advanced deep learning-based TomoSAR imaging method MAda-Net, the proposed method achieves higher imaging accuracy when network training is conducted without prior knowledge of the observation configuration. Additionally, compared to the advanced CS-based TomoSAR imaging method, the proposed method delivers comparable accuracy while improving efficiency by 51.6 times. The code and the data of our paper are available at <span><span>https://github.com/Sunshine-lch/Paper_Geometry-Idenpendent-TomoSAR-imaging.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 608-620"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989652","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":"Joint compression and despeckling by SAR representation learning","authors":"Joel Amao-Oliva ,&nbsp;Nils Foix-Colonier ,&nbsp;Francescopaolo Sica","doi":"10.1016/j.isprsjprs.2024.12.016","DOIUrl":"10.1016/j.isprsjprs.2024.12.016","url":null,"abstract":"<div><div>Synthetic Aperture Radar (SAR) imagery is a powerful and widely used tool in a variety of remote sensing applications. The increasing number of SAR sensors makes it challenging to process and store such a large amount of data. In addition, as the flexibility and processing power of on-board electronics increases, the challenge of effectively transmitting large images to the ground becomes more tangible and pressing. In this paper, we present a method that uses self-supervised despeckling to learn a SAR image representation that is then used to perform image compression. The intuition that despeckling will additionally improve the compression task is based on the fact that the image representation used for despeckling forms an image prior that preserves the main image features while suppressing the spatially correlated noise component. The same learned image representation, which can already be seen as the output of a data reduction task, is further compressed in a lossless manner. While the two tasks can be solved separately, we propose to simultaneously training our model for despeckling and compression in a self-supervised and multi-objective fashion. The proposed network architecture avoids the use of skip connections by ensuring that the encoder and decoder share only the features generated at the lowest network level, namely the bridge, which is then further transformed into a bitstream. This differs from the usual network architectures used for despeckling, such as the commonly used Deep Residual U-Net. In this way, our network design allows compression and reconstruction to be performed at two different times and locations. The proposed method is trained and tested on real data from the TerraSAR-X sensor (downloaded from <span><span>https://earth.esa.int/eogateway/catalog/terrasar-x-esa-archive</span><svg><path></path></svg></span>). The experiments show that joint optimization can achieve performance beyond the state-of-the-art for both despeckling and compression, represented here by the <em>MERLIN</em> and <em>JPEG2000</em> algorithms, respectively. Furthermore, our method has been successfully tested against the cascade of these despeckling and compression algorithms, showing a better spatial and radiometric resolution, while achieving a better compression rate, e.g. a Peak Signal to Noise Ratio (PSNR) always higher than the comparison methods for any achieved bits-per-pixel (BPP) and specifically a PSNR gain of more than 2 dB by a compression rate of 0.7 BPP.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 524-534"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989653","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":"Classification of urban road functional structure by integrating physical and behavioral features","authors":"Qiwen Huang ,&nbsp;Haifu Cui ,&nbsp;Longwei Xiang","doi":"10.1016/j.isprsjprs.2025.01.018","DOIUrl":"10.1016/j.isprsjprs.2025.01.018","url":null,"abstract":"<div><div>Multisource data can extract diverse urban functional features, facilitating a deeper understanding of the functional structure of road networks. Street view images and taxi trajectories, as forms of urban geographic big data, capture features of the urban physical environment and travel behavior, serving as effective data sources for identifying the functional structure of urban spaces. However, street view and taxi trajectory data often suffer from sparse and uneven distributions, and the differences between features are relatively small in the process of multiple feature fusion, which poses significant challenges to accurate classification of road functions. To address these issues, this study proposes the use of the Louvain algorithm and triplet loss methods to enhance features at the community level, resolving the sparse data distribution problem. Simultaneously, the attention mechanism of the graph attention network is applied to dynamically adjust the feature weights within the road network, capturing subtle differences between different features. The experimental results demonstrate that the effectiveness of feature enhancement and capturing differences has improved the accuracy of calculating complex urban road functional structures. Additionally, this study analyzes the degree of mixing and distribution of road functions and explores the relationship between the road functional structure and traffic. The work in this paper assesses urban functional structure at the street level and provides decision-making support for urban planning at a fine scale.</div></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":"220 ","pages":"Pages 753-769"},"PeriodicalIF":10.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072524","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}