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Refined change detection in heterogeneous low-resolution remote sensing images for disaster emergency response ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-14 Di Wang, Guorui Ma, Haiming Zhang, Xiao Wang, Yongxian Zhang
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
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National scale sub-meter mangrove mapping using an augmented border training sample method ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-14 Jinyan Tian, Le Wang, Chunyuan Diao, Yameng Zhang, Mingming Jia, Lin Zhu, Meng Xu, Xiaojuan Li, Huili Gong
This study presents the development of China’s first national-scale sub-meter mangrove map, addressing the need for high-resolution mapping to accurately delineate mangrove boundaries and identify fragmented patches. To overcome the current limitation of 10-m resolution, we developed a novel Semi-automatic Sub-meter Mapping Method (SSMM). The SSMM enhances the spectral separability of mangroves from
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Semantic guided large scale factor remote sensing image super-resolution with generative diffusion prior ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-14 Ce Wang, Wanjie Sun
In the realm of remote sensing, images captured by different platforms exhibit significant disparities in spatial resolution. Consequently, effective large scale factor super-resolution (SR) algorithms are vital for maximizing the utilization of low-resolution (LR) satellite data captured from orbit. However, existing methods confront challenges such as semantic inaccuracies and blurry textures in
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PylonModeler: A hybrid-driven 3D reconstruction method for power transmission pylons from LiDAR point clouds ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-13 Shaolong Wu, Chi Chen, Bisheng Yang, Zhengfei Yan, Zhiye Wang, Shangzhe Sun, Qin Zou, Jing Fu
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
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MLC-net: A sparse reconstruction network for TomoSAR imaging based on multi-label classification neural network ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-13 Depeng Ouyang, Yueting Zhang, Jiayi Guo, Guangyao Zhou
Synthetic Aperture Radar tomography (TomoSAR) has garnered significant interest for its capability to achieve three-dimensional resolution along the elevation angle by collecting a stack of SAR images from different cross-track angles. Compressed Sensing (CS) algorithms have been widely introduced into SAR tomography. However, traditional CS-based TomoSAR methods suffer from weaknesses in noise resistance
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LU5M812TGT: An AI-Powered global database of impact craters [formula omitted] km on the Moon ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-12 Riccardo La Grassa, Elena Martellato, Gabriele Cremonese, Cristina Re, Adriano Tullo, Silvia Bertoli
We release a new global catalog of impact craters on the Moon containing about 5 million craters. Such catalog was derived using a deep learning model, which is based on increasing the spatial image resolution, allowing crater detection down to sizes as small as 0.4 km. Therefore, this database includes ∼69.3% craters with diameter lower than 1 km. The ∼28.7% of the catalog contains mainly craters
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ChangeRD: A registration-integrated change detection framework for unaligned remote sensing images ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-10 Wei Jing, Kaichen Chi, Qiang Li, Qi Wang
Change Detection (CD) is important for natural disaster assessment, urban construction management, ecological monitoring, etc. Nevertheless, the CD models based on the pixel-level classification are highly dependent on the registration accuracy of bi-temporal images. Besides, differences in factors such as imaging sensors and season often result in pseudo-changes in CD maps. To tackle these challenges
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Unwrapping error and fading signal correction on multi-looked InSAR data ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-09 Zhangfeng Ma, Nanxin Wang, Yingbao Yang, Yosuke Aoki, Shengji Wei
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
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PSO-based fine polarimetric decomposition for ship scattering characterization ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-05 Junpeng Wang, Sinong Quan, Shiqi Xing, Yongzhen Li, Hao Wu, Weize Meng
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 loc
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Target-aware attentional network for rare class segmentation in large-scale LiDAR point clouds ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-05 Xinlong Zhang, Dong Lin, Uwe Soergel
Semantic interpretation of 3D scenes poses a formidable challenge in point cloud processing, which also stands as a requisite undertaking across various fields of application involving point clouds. Although a number of point cloud segmentation methods have achieved leading performance, 3D rare class segmentation continues to be a challenge owing to the imbalanced distribution of fine-grained classes
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Overcoming the uncertainty challenges in detecting building changes from remote sensing images ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-12-04 Jiepan Li, Wei He, Zhuohong Li, Yujun Guo, Hongyan Zhang
Detecting building changes with multi-temporal remote sensing (RS) imagery at a very high resolution can help us understand urbanization and human activities, making informed decisions in urban planning, resource allocation, and infrastructure development. However, existing methods for building change detection (BCD) generally overlook critical uncertainty phenomena presented in RS imagery. Specifically
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A real time LiDAR-Visual-Inertial object level semantic SLAM for forest environments ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-30 Hongwei Liu, Guoqi Xu, Bo Liu, Yuanxin Li, Shuhang Yang, Jie Tang, Kai Pan, Yanqiu Xing
The accurate positioning of individual trees, the reconstruction of forest environment in three dimensions and the identification of tree species distribution are crucial aspects of forestry remote sensing. Simultaneous Localization and Mapping (SLAM) algorithms, primarily based on LiDAR or visual technologies, serve as essential tools for outdoor spatial positioning and mapping, overcoming signal
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Location and orientation united graph comparison for topographic point cloud change estimation ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-29 Shoujun Jia, Lotte de Vugt, Andreas Mayr, Chun Liu, Martin Rutzinger
3D topographic point cloud change estimation produces fundamental inputs for understanding Earth surface process dynamics. In general, change estimation aims at detecting the largest possible number of points with significance (i.e., difference > uncertainty) and quantifying multiple types of topographic changes. However, several complex factors, including the inhomogeneous nature of point cloud data
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MGCNet: Multi-granularity consensus network for remote sensing image correspondence pruning ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-28 Fengyuan Zhuang, Yizhang Liu, Xiaojie Li, Ji Zhou, Riqing Chen, Lifang Wei, Changcai Yang, Jiayi Ma
Correspondence pruning aims to remove false correspondences (outliers) from an initial putative correspondence set. This process holds significant importance and serves as a fundamental step in various applications within the fields of remote sensing and photogrammetry. The presence of noise, illumination changes, and small overlaps in remote sensing images frequently result in a substantial number
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Field-scale evaluation of a satellite-based terrestrial biosphere model for estimating crop response to management practices and productivity ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-26 Jingwen Wang, Jose Luis Pancorbo, Miguel Quemada, Jiahua Zhang, Yun Bai, Sha Zhang, Shanxin Guo, Jinsong Chen
Timely and accurate information on crop productivity is essential for characterizing crop growing status and guiding adaptive management practices to ensure food security. Terrestrial biosphere models forced by satellite observations (satellite-TBMs) are viewed as robust tools for understanding large-scale agricultural productivity, with distinct advantages of generalized input data requirement and
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Pansharpening via predictive filtering with element-wise feature mixing ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-26 Yongchuan Cui, Peng Liu, Yan Ma, Lajiao Chen, Mengzhen Xu, Xingyan Guo
Pansharpening is a crucial technique in remote sensing for enhancing spatial resolution by fusing low spatial resolution multispectral (LRMS) images with high spatial panchromatic (PAN) images. Existing deep convolutional networks often face challenges in capturing fine details due to the homogeneous operation of convolutional kernels. In this paper, we propose a novel predictive filtering approach
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A UAV-based sparse viewpoint planning framework for detailed 3D modelling of cultural heritage monuments ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-26 Zebiao Wu, Patrick Marais, Heinz Rüther
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
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Optimizing hybrid models for canopy nitrogen mapping from Sentinel-2 in Google Earth Engine ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-22 Emma De Clerck, Dávid D.Kovács, Katja Berger, Martin Schlerf, Jochem Verrelst
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
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A unique dielectric constant estimation for lunar surface through PolSAR model-based decomposition ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-22 Inderkumar Kochar, Anup Das, Rajib Kumar Panigrahi
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
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Unwrap-Net: A deep neural network-based InSAR phase unwrapping method assisted by airborne LiDAR data ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-21 Wang Yang, Yi He, Qing Zhu, Lifeng Zhang, Long Jin
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
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METNet: A mesh exploring approach for segmenting 3D textured urban scenes ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-21 Qendrim Schreiber, Nicola Wolpert, Elmar Schömer
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
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On-orbit geometric calibration of MERSI whiskbroom scanner ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-18 Hongbo Pan, Xue Zhang, Zixuan Liu, Tao Huang
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
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ACMatch: Improving context capture for two-view correspondence learning via adaptive convolution ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-16 Xiang Fang, Yifan Lu, Shihua Zhang, Yining Xie, Jiayi Ma
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
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A universal adapter in segmentation models for transferable landslide mapping ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-15 Ruilong Wei, Yamei Li, Yao Li, Bo Zhang, Jiao Wang, Chunhao Wu, Shunyu Yao, Chengming Ye
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
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Contrastive learning for real SAR image despeckling ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-15 Yangtian Fang, Rui Liu, Yini Peng, Jianjun Guan, Duidui Li, Xin Tian
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
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MIWC: A multi-temporal image weighted composition method for satellite-derived bathymetry in shallow waters ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-15 Zhixin Duan, Liang Cheng, Qingzhou Mao, Yueting Song, Xiao Zhou, Manchun Li, Jianya Gong
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
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Common-feature-track-matching approach for multi-epoch UAV photogrammetry co-registration ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-14 Xinlong Li, Mingtao Ding, Zhenhong Li, Peng Cui
Automatic co-registration of multi-epoch Unmanned Aerial Vehicle (UAV) image sets remains challenging due to the radiometric differences in complex dynamic scenes. Specifically, illumination changes and vegetation variations usually lead to insufficient and spatially unevenly distributed common tie points (CTPs), resulting in under-fitting of co-registration near the areas without CTPs. In this paper
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B3-CDG: A pseudo-sample diffusion generator for bi-temporal building binary change detection ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-14 Peng Chen, Peixian Li, Bing Wang, Sihai Zhao, Yongliang Zhang, Tao Zhang, Xingcheng Ding
Building change detection (CD) plays a crucial role in urban planning, land resource management, and disaster monitoring. Currently, deep learning has become a key approach in building CD, but challenges persist. Obtaining large-scale, accurately registered bi-temporal images is difficult, and annotation is time-consuming. Therefore, we propose B3-CDG, a bi-temporal building binary CD pseudo-sample
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Mesh refinement method for multi-view stereo with unary operations ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-12 Jianchen Liu, Shuang Han, Jin Li
3D reconstruction is an important part of digital city, high-accuracy 3D modeling method has been widely studied as an important pathway to visualizing 3D city scenes. However, the problems of image resolution, noise, and occlusion result in low quality and smooth features in the mesh model. Therefore, the model needs to be refined to improve the mesh quality and enhance the visual effect. This paper
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Fast and accurate SAR geocoding with a plane approximation ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-11 Shaokun Guo, Jie Dong, Yian Wang, Mingsheng Liao
Geocoding is the procedure of finding the mapping between the Synthetic Aperture Radar (SAR) image and the imaged scene. The inverse form of the Range-Doppler (RD) model has been adopted to approximate the geocoding results. However, with advances in SAR imaging geodesy, its imprecise nature becomes more perceptible. The forward RD model gives reliable solutions but is time-consuming and unable to
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3D point cloud regularization method for uniform mesh generation of mining excavations ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-09 Przemysław Dąbek, Jacek Wodecki, Paulina Kujawa, Adam Wróblewski, Arkadiusz Macek, Radosław Zimroz
Mine excavation systems are usually dozens of kilometers long with varying geometry on a small scale (roughness and shape of the walls) and on a large scale (varying widths of the tunnels, turns, and crossings). In this article, the authors address the problem of analyzing laser scanning data from large mining structures that can be used for various purposes, with focus on ventilation simulations.
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Generalization in deep learning-based aircraft classification for SAR imagery ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-08 Andrea Pulella, Francescopaolo Sica, Carlos Villamil Lopez, Harald Anglberger, Ronny Hänsch
Automatic Target Recognition (ATR) from Synthetic Aperture Radar (SAR) data covers a wide range of applications. SAR ATR helps to detect and track vehicles and other objects, e.g. in disaster relief and surveillance operations. Aircraft classification covers a significant part of this research area, which differs from other SAR-based ATR tasks, such as ship and ground vehicle detection and classification
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HDRSA-Net: Hybrid dynamic residual self-attention network for SAR-assisted optical image cloud and shadow removal ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-07 Jun Pan, Jiangong Xu, Xiaoyu Yu, Guo Ye, Mi Wang, Yumin Chen, Jianshen Ma
Clouds and shadows often contaminate optical remote sensing images, resulting in missing information. Consequently, continuous spatiotemporal monitoring of the Earth’s surface requires the efficient removal of clouds and shadows. Unlike optical satellites, synthetic aperture radar (SAR) has active imaging capabilities in all weather conditions, supplying valuable supplementary information for reconstructing
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Advancing mangrove species mapping: An innovative approach using Google Earth images and a U-shaped network for individual-level Sonneratia apetala detection ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-07 Chuanpeng Zhao, Yubin Li, Mingming Jia, Chengbin Wu, Rong Zhang, Chunying Ren, Zongming Wang
The exotic mangrove species Sonneratia apetala has been colonizing coastal China for several decades, sparking attention and debates from the public and policy-makers about its reproduction, dispersal, and spread. Existing local-scale studies have relied on fine but expensive data sources to map mangrove species, limiting their applicability for detecting S. apetala in large areas due to cost constraints
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A multi-view graph neural network for building age prediction ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-07 Yi Wang, Yizhi Zhang, Quanhua Dong, Hao Guo, Yingchun Tao, Fan Zhang
Building age is crucial for inferring building energy consumption and understanding the interactions between human behavior and urban infrastructure. Limited by the challenges of surveys, some machine learning methods have been utilized to predict and fill in missing building age data using building footprint. However, the existing methods lack explicit modeling of spatial effects and semantic relationships
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Word2Scene: Efficient remote sensing image scene generation with only one word via hybrid intelligence and low-rank representation ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-06 Jiaxin Ren, Wanzeng Liu, Jun Chen, Shunxi Yin, Yuan Tao
To address the numerous challenges existing in current remote sensing scene generation methods, such as the difficulty in capturing complex interrelations among geographical features and the integration of implicit expert knowledge into generative models, this paper proposes an efficient method for generating remote sensing scenes using hybrid intelligence and low-rank representation, named Word2Scene
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Selective weighted least square and piecewise bilinear transformation for accurate satellite DSM generation ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-06 Nazila Mohammadi, Amin Sedaghat
One of the main products of multi-view stereo (MVS) high-resolution satellite (HRS) images in photogrammetry and remote sensing is digital surface model (DSM). Producing DSMs from MVS HRS images still faces serious challenges due to various reasons such as complexity of imaging geometry and exterior orientation model in HRS, as well as large dimensions and various geometric and illumination variations
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Integrating synthetic datasets with CLIP semantic insights for single image localization advancements ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-06 Dansheng Yao, Mengqi Zhu, Hehua Zhu, Wuqiang Cai, Long Zhou
Accurate localization of pedestrians and mobile robots is critical for navigation, emergency response, and autonomous driving. Traditional localization methods, such as satellite signals, often prove ineffective in certain environments, and acquiring sufficient positional data can be challenging. Single image localization techniques have been developed to address these issues. However, current deep
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A_OPTRAM-ET: An automatic optical trapezoid model for evapotranspiration estimation and its global-scale assessments ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-11-02 Zhaoyuan Yao, Wangyipu Li, Yaokui Cui
Remotely sensed evapotranspiration (ET) at a high spatial resolution (30 m) has wide-ranging applications in agriculture, hydrology and meteorology. The original optical trapezoid model for ET (O_OPTRAM-ET), which does not require thermal remote sensing, shows potential for high-resolution ET estimation. However, the non-automated O_OPTRAM-ET heavily depends on visual interpretation or optimization
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Atmospheric correction of geostationary ocean color imager data over turbid coastal waters under high solar zenith angles ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-31 Hao Li, Xianqiang He, Palanisamy Shanmugam, Yan Bai, Xuchen Jin, Zhihong Wang, Yifan Zhang, Difeng wang, Fang Gong, Min Zhao
The traditional atmospheric correction models employed with the near-infrared iterative schemes inaccurately estimate aerosol radiance at high solar zenith angles (SZAs), leading to a substantial loss of valid products for dawn or dusk observations by the geostationary satellite ocean color sensor. To overcome this issue, we previously developed an atmospheric correction model suitable for open ocean
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Cascaded recurrent networks with masked representation learning for stereo matching of high-resolution satellite images ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-30 Zhibo Rao, Xing Li, Bangshu Xiong, Yuchao Dai, Zhelun Shen, Hangbiao Li, Yue Lou
Stereo matching of satellite images presents challenges due to missing data, domain differences, and imperfect rectification. To address these issues, we propose cascaded recurrent networks with masked representation learning for high-resolution satellite stereo images, consisting of feature extraction and cascaded recurrent modules. First, we develop the correlation computation in the cascaded recurrent
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Bridging real and simulated data for cross-spatial- resolution vegetation segmentation with application to rice crops ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-28 Yangmingrui Gao, Linyuan Li, Marie Weiss, Wei Guo, Ming Shi, Hao Lu, Ruibo Jiang, Yanfeng Ding, Tejasri Nampally, P. Rajalakshmi, Frédéric Baret, Shouyang Liu
Accurate image segmentation is essential for image-based estimation of vegetation canopy traits, as it minimizes background interference. However, existing segmentation models often lack the generalization ability to effectively tackle both ground-based and aerial images across a wide range of spatial resolutions. To address this limitation, a cross-spatial-resolution image segmentation model for rice
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Cross-modal change detection using historical land use maps and current remote sensing images ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-24 Kai Deng, Xiangyun Hu, Zhili Zhang, Bo Su, Cunjun Feng, Yuanzeng Zhan, Xingkun Wang, Yansong Duan
Using bi-temporal remote sensing imagery to detect land in urban expansion has become a common practice. However, in the process of updating land resource surveys, directly detecting changes between historical land use maps (referred to as “maps” in this paper) and current remote sensing images (referred to as “images” in this paper) is more direct and efficient than relying on bi-temporal image comparisons
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Nighttime fog and low stratus detection under multi-scene and all lunar phase conditions using S-NPP/VIIRS visible and infrared channels ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-21 Jun Jiang, Zhigang Yao, Yang Liu
A scheme for satellite remote sensing is proposed to detect nighttime fog and low stratus (FLS) by combining visible, mid-infrared, and far-infrared channels. The S-NPP/VIIRS dataset and ERA5 reanalysis data are primarily used, and a comprehensive threshold system is established through statistical analysis, simulation calculations, and sensitivity experiments. 98 cases of nighttime FLS occurring from
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PRISMethaNet: A novel deep learning model for landfill methane detection using PRISMA satellite data ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-20 Mohammad Marjani, Fariba Mohammadimanesh, Daniel J. Varon, Ali Radman, Masoud Mahdianpari
Methane (CH4) is one of the most significant greenhouse gases responsible for about one-third of climate warming since preindustrial times, originating from various sources. Landfills are responsible for a large percentage of CH4 emissions, and population growth can boost these emissions. Therefore, it is vital to automate the process of CH4 monitoring over landfills. This study proposes a convolutional
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Generalized spatio-temporal-spectral integrated fusion for soil moisture downscaling ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-19 Menghui Jiang, Huanfeng Shen, Jie Li, Liangpei Zhang
Soil moisture (SM) is one of the key land surface parameters, but the coarse spatial resolution of the passive microwave SM products constrains the precise monitoring of surface changes. The existing SM downscaling methods typically either utilize spatio-temporal information or leverage auxiliary parameters, without fully mining the complementary information between them. In this paper, a generalized
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Improving crop type mapping by integrating LSTM with temporal random masking and pixel-set spatial information ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-19 Xinyu Zhang, Zhiwen Cai, Qiong Hu, Jingya Yang, Haodong Wei, Liangzhi You, Baodong Xu
Accurate and timely crop type classification is essential for effective agricultural monitoring, cropland management, and yield estimation. Unfortunately, the complicated temporal patterns of different crops, combined with gaps and noise in satellite observations caused by clouds and rain, restrict crop classification accuracy, particularly during early seasons with limited temporal information. Although
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Detection of buildings with potential damage using differential deformation maps ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-18 Saeedeh Shahbazi, Anna Barra, Qi Gao, Michele Crosetto
The European Ground Motion Service (EGMS) is a crucial component of the systematic monitoring and quantification of land displacement across Europe. By using Sentinel-1 full-resolution images, EGMS offers a reliable, consistent, and annually updated dataset for detecting natural and anthropogenic ground motion phenomena. While the Copernicus platform grants free accessibility to EGMS displacement maps
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PolyR-CNN: R-CNN for end-to-end polygonal building outline extraction ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-18 Weiqin Jiao, Claudio Persello, George Vosselman
Polygonal building outline extraction has been a research focus in recent years. Most existing methods have addressed this challenging task by decomposing it into several subtasks and employing carefully designed architectures. Despite their accuracy, such pipelines often introduce inefficiencies during training and inference. This paper presents an end-to-end framework, denoted as PolyR-CNN, which
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Deep shared proxy construction hashing for cross-modal remote sensing image fast target retrieval ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-18 Lirong Han, Mercedes E. Paoletti, Sergio Moreno-Álvarez, Juan M. Haut, Antonio Plaza
The diversity of remote sensing (RS) image modalities has expanded alongside advancements in RS technologies. A plethora of optical, multispectral, and hyperspectral RS images offer rich geographic class information. The ability to swiftly access multiple RS image modalities is crucial for fully harnessing the potential of RS imagery. In this work, an innovative method, called Deep Shared Proxy Construction
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Multi-level urban street representation with street-view imagery and hybrid semantic graph ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-18 Yan Zhang, Yong Li, Fan Zhang
Street-view imagery has been densely covering cities. They provide a close-up perspective of the urban physical environment, allowing a comprehensive perception and understanding of cities. There has been a significant amount of effort to represent the urban physical environment based on street view imagery, and this representation has been utilized to study the relationships between the physical environment
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A robust method for mapping soybean by phenological aligning of Sentinel-2 time series ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-17 Xin Huang, Anton Vrieling, Yue Dou, Mariana Belgiu, Andrew Nelson
Soybean is an important crop for food and animal feed. Production and area both continue to increase and expand into new areas and countries. Spatially explicit information on soybean cultivation is essential to crop monitoring, production estimation, and national accounting systems. However, its cultivation in diverse climate conditions, landscapes, and agricultural systems poses challenges to accurately
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Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-15 Zhijia Zheng, Xiuyuan Zhang, Jiajun Li, Eslam Ali, Jinsongdi Yu, Shihong Du
Sand dune patterns (SDPs) are spatial aggregations of dunes and interdunes, exhibiting distinct morphologies and spatial structures. Recognizing global SDPs is crucial for understanding the development processes, contributing factors, and self-organization characteristics of aeolian systems. However, the diversity, complexity, and multiscale nature of global SDPs poses significant technical challenges
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A highly efficient index for robust mapping of tidal flats from sentinel-2 images directly ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-12 Pengfei Tang, Shanchuan Guo, Peng Zhang, Lu Qie, Xiaoquan Pan, Jocelyn Chanussot, Peijun Du
As an essential component of the intertidal zone, tidal flats (TFs) are areas rich in resources where with the most intense material and energy exchanges. However, due to the dual threats of human activities and extreme climate conditions, TFs are disappearing on a large scale. Despite their importance, accurately mapping TFs has proved challenging due to their complex and dynamic nature. Nevertheless
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Mineral detection based on hyperspectral remote sensing imagery on Mars: From detection methods to fine mapping ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-12 Tian Ke, Yanfei Zhong, Mi Song, Xinyu Wang, Liangpei Zhang
Hyperspectral remote sensing is a commonly used technical means for mineral detection on the Martian surface, which has important implications for the study of Martian geological evolution and the study for potential biological signatures. The increasing volume of Martian remote sensing data and complex issues such as the intimate mixture of Martian minerals make research on Martian mineral detection
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Improving drone-based uncalibrated estimates of wheat canopy temperature in plot experiments by accounting for confounding factors in a multi-view analysis ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-11 Simon Treier, Juan M. Herrera, Andreas Hund, Norbert Kirchgessner, Helge Aasen, Achim Walter, Lukas Roth
Canopy temperature (CT) is an integrative trait, indicative of the relative fitness of a plant genotype to the environment. Lower CT is associated with higher yield, biomass and generally a higher performing genotype. In view of changing climatic conditions, measuring CT is becoming increasingly important in breeding and variety testing. Ideally, CTs should be measured as simultaneously as possible
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PolyGNN: Polyhedron-based graph neural network for 3D building reconstruction from point clouds ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-10 Zhaiyu Chen, Yilei Shi, Liangliang Nan, Zhitong Xiong, Xiao Xiang Zhu
We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by polyhedral decomposition via graph node classification, achieving a watertight and compact reconstruction. To effectively represent arbitrary-shaped polyhedra in the neural network, we propose a skeleton-based sampling strategy to generate polyhedron-wise
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SAMPolyBuild: Adapting the Segment Anything Model for polygonal building extraction ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-10 Chenhao Wang, Jingbo Chen, Yu Meng, Yupeng Deng, Kai Li, Yunlong Kong
Extracting polygonal buildings from high-resolution remote sensing images is a critical task for large-scale mapping, 3D city modeling, and various geographic information system applications. Traditional methods are often restricted in accurately delineating boundaries and exhibit limited generalizability, which can affect their real-world applicability. The Segment Anything Model (SAM), a promptable
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Clustering, triangulation, and evaluation of 3D lines in multiple images ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-08 Dong Wei, Haoyu Guo, Yi Wan, Yongjun Zhang, Chang Li, Guangshuai Wang
Three-dimensional (3D) lines require further enhancement in both clustering and triangulation. Line clustering assigns multiple image lines to a single 3D line to eliminate redundant 3D lines. Currently, it depends on the fixed and empirical parameter. However, a loose parameter could lead to over-clustering, while a strict one may cause redundant 3D lines. Due to the absence of the ground truth, the
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HyperDehazing: A hyperspectral image dehazing benchmark dataset and a deep learning model for haze removal ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-10-05 Hang Fu, Ziyan Ling, Genyun Sun, Jinchang Ren, Aizhu Zhang, Li Zhang, Xiuping Jia
Haze contamination severely degrades the quality and accuracy of optical remote sensing (RS) images, including hyperspectral images (HSIs). Currently, there are no paired benchmark datasets containing hazy and haze-free scenes in HSI dehazing, and few studies have analyzed the distributional properties of haze in the spatial and spectral domains. In this paper, we developed a new hazy synthesis strategy