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On the reachability and genesis of water ice on the Moon ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-23 Tathagata Chakraborty, Tajdarul Hassan Syed, Essam Heggy, Deepak Putrevu, Upama Dutta
Understanding the reachability of water ice by future in-situ experiments near the lunar poles is crucial for supporting growing exploration plans and constraining the uncertainties on its genesis and distribution. To achieve this objective, we perform a thorough three-dimensional mapping of the distribution of water ice in the lunar polar regions (70° onward), integrating radar, optical, and neutron
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First retrieval of daily 160 m aerosol optical depth over urban areas using Gaofen-1/6 synergistic observations: Algorithm development and validation ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-22 Jiadan Dong, Tianhao Zhang, Lunche Wang, Zhengqiang Li, Man Sing Wong, Muhammad Bilal, Zhongmin Zhu, Feiyue Mao, Xinghui Xia, Ge Han, Qiangqiang Xu, Yu Gu, Yun Lin, Bin Zhao, Zhiwei Li, Kai Xu, Xiaoling Chen, Wei Gong
The satellite-based aerosol optical depth (AOD), which can provide continuous spatial observations of aerosol loadings, is widely adopted to estimate atmospheric environmental quality and evaluate its risk for human health. However, current satellite-retrieved AOD products characterized by a comparatively coarse spatial resolution (≥1 km) can hardly analyze the structure of atmospheric pollution or
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Integrating physical model and image simulations to correct topographic effects on surface reflectance ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-22 Wentao Yu, Huabing Huang, Qiang Liu, Jie Wang
Topography complicates the illumination distribution over rugged terrains and hinders the applications of surface reflectance data over mountainous areas. Topographic correction is an essential process to remove the topographic effects in surface reflectance data. This study proposed a Physical model and image Simulation-based topographic Correction method (PSC) for atmospherically corrected surface
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Making satellite-derived empirical bathymetry independent of high-quality in-situ depth data: An assessment of four possible model calibration data ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-20 Bin Cao, Hui Liu, Bincai Cao
The empirical approach of satellite-derived bathymetry provides a straightforward, easily-implemented, and very effective way of estimating shallow-water depths from high-spatial-resolution satellite images. However, this approach has a great challenge that it requires high-quality in-situ depth data, which often do not exist or are not available due to financial and/or technical reasons, as the depth
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Semantic change detection using a hierarchical semantic graph interaction network from high-resolution remote sensing images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-20 Jiang Long, Mengmeng Li, Xiaoqin Wang, Alfred Stein
Current semantic change detection (SCD) methods face challenges in modeling temporal correlations (TCs) between bitemporal semantic features and difference features. These methods lead to inaccurate detection results, particularly for complex SCD scenarios. This paper presents a hierarchical semantic graph interaction network (HGINet) for SCD from high-resolution remote sensing images. This multitask
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The ClearSCD model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-18 Kai Tang, Fei Xu, Xuehong Chen, Qi Dong, Yuheng Yuan, Jin Chen
The Earth has been undergoing continuous anthropogenic and natural change. High spatial resolution (HSR) remote sensing imagery provides a unique opportunity to accurately reveal these changes on a planetary scale. Semantic change detection (SCD) with HSR imagery has become a common technique for tracking the evolution of land surface types at a semantic level. However, existing SCD methods rarely
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Corrigendum to “ReCuSum: A polyvalent method to monitor tropical forest disturbances” [ISPRS J. Photogramm. Rem. Sens. 203 (2023) 358–372] ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-18 Bertrand Ygorra, Frédéric Frappart, Jean-Pierre Wigneron, Christophe Moisy, Thibault Catry, Benjamin Pillot, Jonas Courtalon, Anna Kharlanova, Serge Riazanoff
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Enhanced wavelet based spatiotemporal fusion networks using cross-paired remote sensing images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-17 Xingjian Zhang, Shuang Li, Zhenyu Tan, Xinghua Li
Spatiotemporal fusion can provide remote sensing images with both high temporal and high spatial resolution for earth observation applications. Most of the state-of-the-art models require three or even five images as input, which may lead to difficulties in practical applications due to bad weather or data missing. In this paper, the enhanced cross-paired wavelet based spatiotemporal fusion networks
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Forecasting corn NDVI through AI-based approaches using sentinel 2 image time series ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-17 A. Farbo, F. Sarvia, S. De Petris, V. Basile, E. Borgogno-Mondino
Precision Agriculture (PA) has revolutionized crop management by leveraging information technology, satellite positioning data, and remote sensing. One crucial component in PA applications is the Normalized Difference Vegetation Index (NDVI), which offers valuable insights into crop vigor and health. However, discontinuity of optical satellite acquisitions related to cloud cover and the huge load of
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Robust remote sensing retrieval of key eutrophication indicators in coastal waters based on explainable machine learning ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-17 Liudi Zhu, Tingwei Cui, Runa A, Xinliang Pan, Wenjing Zhao, Jinzhao Xiang, Mengmeng Cao
Excessive discharges of nitrogen and phosphorus nutrients lead to eutrophication in coastal waters. Optical remote sensing retrieval of the key eutrophication indicators, namely dissolved inorganic nitrogen concentration (DIN), soluble reactive phosphate concentration (SRP), and chemical oxygen demand (COD), remains challenging due to lack of distinct spectral features. Although machine learning (ML)
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A Variance-Covariance method to estimating the errors of 3-D ground displacement time-series using small baseline InSAR algorithms and multi-platform SAR data ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-12 Francesco Falabella, Antonio Pepe, Angela Perrone, Tony Alfredo Stabile
The joint exploitation of complementary information from independent satellite and ground-based SAR observations can allow reconstructing the three-dimensional (up-down, east–west, north–south) ground displacement profile. Some attempts have recently been made to complement satellite and ground-based SAR (GB-SAR) data. However, a method for generating the 3-D ground displacement time-series and evaluating
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HCTO: Optimality-aware LiDAR inertial odometry with hybrid continuous time optimization for compact wearable mapping system ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-12 Jianping Li, Shenghai Yuan, Muqing Cao, Thien-Minh Nguyen, Kun Cao, Lihua Xie
Compact wearable mapping system (WMS) has gained significant attention due to their convenience in various applications. Specifically, it provides an efficient way to collect prior maps for 3D structure inspection and robot-based “last-mile delivery” in complex environments. However, vibrations in human motion and the uneven distribution of point cloud features in complex environments often lead to
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Unsupervised shape-aware SOM down-sampling for plant point clouds ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-10 Dawei Li, Zhaoyi Zhou, Yongchang Wei
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Efficient structure from motion for UAV images via anchor-free parallel merging ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-09 San Jiang, Yichen Ma, Wanshou Jiang, Qingquan Li
This paper primarily presents a parallel incremental Structure from Motion (ISfM) solution for large-scale images captured by unmanned aerial vehicles (UAVs). The core ideas are a local connection-constrained edge weighting strategy for match graph construction and an anchor-free parallel merging algorithm for the merged model generation. First, an effective algorithm is employed to retrieve spatially
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Corrigendum to “Ground subsidence in Tucson, Arizona, monitored by time-series analysis using multi-sensor InSAR datasets from 1993 to 2011” [ISPRS J. Photogramm. Remote Sens. 107 (2015) 126–141] ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-09 Jin-Woo Kim, Zhong Lu, Yuanyuan Jia, C.K. Shum
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Joint target and background temporal propagation for aerial tracking ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-09 Xu Lei, Wensheng Cheng, Chang Xu, Wen Yang
Tracking objects from aerial imagery is significant in numerous remote sensing-based applications, including environmental monitoring, security surveillance, and search & rescue. However, tracking specific targets in aerial images is still challenging due to target appearance variation and similar object distraction. To address these challenges, we propose a joint target and background temporal propagation
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AiTARs-Net: A novel network for detecting arbitrary-oriented transverse aeolian ridges from Tianwen-1 HiRIC images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-09 Zhen Cao, Zhizhong Kang, Teng Hu, Ze Yang, Dong Chen, Xiaolan Ren, Qingyu Meng, Dong Wang
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The SAR2Height framework for urban height map reconstruction from single SAR intensity images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-08 Michael Recla, Michael Schmitt
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A satellite-field phenological bridging framework for characterizing community-level spring forest phenology using multi-scale satellite imagery ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-06 Chunyuan Diao, Carol K. Augspurger, Yilun Zhao, Carl F. Salk
Forest phenology, as a sensitive indicator of a forest’s response to climate change and variability, has long been monitored using remote sensing, yet has seldom been interpreted or validated with spatially compatible, community-level field phenological observations. In temperate deciduous forests, multiple spring phenological phases are critical for modeling carbon storage and biogeochemical cycles
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Plant-Denoising-Net (PDN): A plant point cloud denoising network based on density gradient field learning ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-04 Jianeng Wu, Lirong Xiang, Hui You, Lie Tang, Jingyao Gai
Effective point cloud denoising is critical in 3D plant phenotyping applications, which reduces interference in subsequent algorithms and improves the accuracy of plant phenotypes measurement. Deep learning-based point cloud denoising algorithms have shown excellent denoising performance on simple CAD models. However, these algorithms suffer from issues including over-smoothing or shrinkage and low
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Maize stem–leaf segmentation framework based on deformable point clouds ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-03 Xin Yang, Teng Miao, Xueying Tian, Dabao Wang, Jianxiang Zhao, Lili Lin, Chao Zhu, Tao Yang, Tongyu Xu
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Global mapping of fractional tree cover for forest cover change analysis ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-03 Yang Liu, Ronggao Liu, Lin Qi, Jilong Chen, Jinwei Dong, Xuexin Wei
Fractional tree cover facilitates the characterization of forest cover changes using satellite data. However, there are still substantial challenges in generating fractional tree cover datasets that satisfy the requirements of interannual stability for forest cover change monitoring. In this study, a global annual fractional tree cover dataset, named as GLOBMAP Fractional Tree Cover, was generated
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PAL-SLAM2: Visual and visual–inertial monocular SLAM for panoramic annular lens ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-03 Ding Wang, Junhua Wang, Yuhan Tian, Yi Fang, Zheng Yuan, Min Xu
This paper presents PAL-SLAM2, a visual and visual–inertial monocular simultaneous localization and mapping (SLAM) system for a panoramic annular lens (PAL) with an ultra-hemispherical field of view (FoV), overcoming the limitations of traditional frameworks in handling fast turns, nighttime conditions and rapid lighting changes. The system incorporates modules for initialization, tracking, local mapping
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Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-02 Jian Cheng, Changjian Deng, Yanzhou Su, Zeyu An, Qi Wang
Unmanned Aerial Vehicle (UAV) has seen a dramatic rise in popularity for remote-sensing image acquisition and analysis in recent years. It has brought promising results in low-altitude monitoring tasks that require detailed visual inspections. Semantic segmentation is one of the hot topics in UAV remote sensing image analysis, as its capability to mine contextual semantic information from UAV images
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Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-30 Juan Skobalski, Vasit Sagan, Haireti Alifu, Omar Al Akkad, Felipe A. Lopes, Fernando Grignola
Despite significant progress has been made towards crop yield prediction with remote sensing, there exist knowledge gaps on (1) the impacts of temporal resolution of imaging frequencies on yield prediction, (2) transferability of the models among different genotypes and test sites, and (3) translation of these research developments to crop breeding that benefit farmers. Existing research predominantly
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Evaluation of PlanetScope-detected plant-specific phenology using infrared-enabled PhenoCam observations in semi-arid ecosystems ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-29 Yuxia Liu, Xiaoyang Zhang, Yu Shen, Yongchang Ye, Shuai Gao, Khuong H. Tran
Phenology detection from remotely sensed data remains challenging in semi-arid ecosystems due to the unique spatial heterogeneity and irregular temporal growth in plants. PlanetScope imagery, with fine spatial and temporal resolutions, is revolutionizing the earth observation sector. It has demonstrated its effectiveness in monitoring phenology dynamics across various terrestrial ecosystems. However
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Changes in the Team of Associate Editors ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-26 Qihao Weng, Clément Mallet
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A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-25 Julian Merder, Gang Zhao, Nima Pahlevan, Robert A. Rigby, Dimitrios M. Stasinopoulos, Anna M. Michalak
The ability to infer ocean chlorophyll- concentrations (Chl) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global high-performance liquid chromatography (HPLC) data that leverages Level-3 remote sensing reflectance () products from the Moderate Resolution Imaging
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Satellite video single object tracking: A systematic review and an oriented object tracking benchmark ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-25 Yuzeng Chen, Yuqi Tang, Yi Xiao, Qiangqiang Yuan, Yuwei Zhang, Fengqing Liu, Jiang He, Liangpei Zhang
Single object tracking (SOT) in satellite video (SV) enables the continuous acquisition of position and range information of an arbitrary object, showing promising value in remote sensing applications. However, existing trackers and datasets rarely focus on the SOT of oriented objects in SV. To bridge this gap, this article presents a comprehensive review of various tracking paradigms and frameworks
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Recognition for SAR deformation military target from a new MiniSAR dataset using multi-view joint transformer approach ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-21 Jiming Lv, Daiyin Zhu, Zhe Geng, Shengliang Han, Yu Wang, Zheng Ye, Tao Zhou, Hongren Chen, Jiawei Huang
Accurately detecting ground armored weapons is crucial for achieving initiative advantages in military operations. Generally, satellite or airborne synthetic aperture radar (SAR) systems face limitations due to their revisit cycles and fixed flight trajectories, resulting in single-view imaging of targets, thereby hampering the recognition of small SAR ground targets. In contrast, MiniSAR possesses
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An enhanced large-scale benthic reflectance retrieval model for the remote sensing of submerged ecosystems in optically shallow waters ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-20 Yuxin Wang, Xianqiang He, Palanisamy Shanmugam, Yan Bai, Teng Li, Difeng Wang, Qiankun Zhu, Fang Gong
Shallow water benthic habitats have been significantly degraded and seriously threatened by intensifying climate changes and anthropogenic stressors. Benthic reflectance () of optically shallow waters (OSWs) is a key parameter for remote sensing of benthic habitats’ composition and health status. The remote sensing reflectance () just above the water surface contains the coupled spectral information
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WPS:A whole phenology-based spectral feature selection method for mapping winter crop from time-series images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-20 Man Liu, Wei He, Hongyan Zhang
Accurately obtaining the spatial distribution and planting patterns of crops is very important for agricultural planning and food security. At present, time-series images have been proved to be an effective tool to characterize crop seasonal growth patterns, and identifying crop information by measuring the time-series similarity between unknown classes and known crop phenology curves is also considered
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A robust data-model dual-driven fusion with uncertainty estimation for LiDAR–IMU localization system ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-18 Qipeng Li, Yuan Zhuang, Jianzhu Huai, Xuan Wang, Binliang Wang, Yue Cao
Accurate and robust localization is a critical requirement for autonomous driving and intelligent robots, particularly in complex dynamic environments and various motion scenarios. However, existing LiDAR odometry methods often struggle to promptly respond to changes in the surroundings and motion conditions with fixed parameters through execution, hindering their ability to adaptively adjust system
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Enhancing deforestation monitoring in the Brazilian Amazon: A semi-automatic approach leveraging uncertainty estimation ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-14 Jorge Andres Chamorro Martinez, Gilson A. Ostwald Pedro da Costa, Cassiano Gustavo Messias, Luciana de Souza Soler, Claudio A. de Almeida, Raul Queiroz Feitosa
Official governmental monitoring of deforestation in the Brazilian Amazon relies on human experts conducting visual analyzes of remote sensing images, an approach that is very expensive and time-consuming due to the enormous geographic extent to be inspected. The main obstacle to the adoption of fully automatic methods is the requirement for the highest possible deforestation detection accuracy, which
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Semantics-enhanced discriminative descriptor learning for LiDAR-based place recognition ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-14 Yiwen Chen, Yuan Zhuang, Jianzhu Huai, Qipeng Li, Binliang Wang, Nashwa El-Bendary, Alper Yilmaz
LiDAR-based place recognition (LPR) aims to localize autonomous vehicles and mobile robots relative to pre-built maps or retrieve previously visited places. However, the complexity of real-world scenes and changes in viewpoint are significant challenges for place recognition. As high-level information, semantics makes it easier to distinguish geometrically similar scene situations. Unlike most existing
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The One-Point-One-Line geometry for robust and efficient line segment correspondence ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-13 Haoyu Guo, Dong Wei, Yongjun Zhang, Yi Wan, Zhi Zheng, Yongxiang Yao, Xinyi Liu, Zhuofan Li
Three-dimensional (3D) lines are common elements in artificial scenes and serve as basic, yet essential features for structural 3D reconstruction. The crucial step of 3D line reconstruction, namely two-view line segment matching, still faces challenges in terms of both accuracy and efficiency improvements. Therefore, robust and efficient constraints are needed to establish valid line candidates. This
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Quantifying the impact of urban trees on land surface temperature in global cities ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-12 Tingting He, Yihua Hu, Andong Guo, Yuwei Chen, Jun Yang, Mengmeng Li, Maoxin Zhang
Urban trees are not only a core component of natural infrastructure but also an effective way to mitigate urban heat with nature-based solutions. Comprehensively revealing the cooling effects of trees and their drivers is valuable for enhancing urban climate resilience and promoting sustainable development. While existing studies have investigated the cooling effects of two-dimensional characteristics
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Evaluation of Landsat-9 interoperability with Sentinel-2 and Landsat-8 over Europe and local comparison with field surveys ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-10 F. Trevisiol, E. Mandanici, A. Pagliarani, G. Bitelli
The recent launch of Landsat-9 satellite enriches the opportunities to work with dense time series of multispectral medium-resolution images. The integration of Landsat-9 in a multi-constellation series with Landsat-8 and Sentinel-2 requires a harmonization of the surface reflectance values that can be obtained from the official Level-2 products. This paper proposes the coefficients of the optimal
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Detecting Marine pollutants and Sea Surface features with Deep learning in Sentinel-2 imagery ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-07 Katerina Kikaki, Ioannis Kakogeorgiou, Ibrahim Hoteit, Konstantinos Karantzalos
Despite the significant negative impact of marine pollution on the ecosystem and humans, its automated detection and tracking from the broadly available satellite data is still a major challenge. In particular, most research and development efforts focus on one specific pollutant implementing, in most cases, binary classification tasks, e.g., detect or no , or target a limited number of classes, such
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Low-altitude remote sensing-based global 3D path planning for precision navigation of agriculture vehicles - beyond crop row detection ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-05 Dongfang Li, Boliao Li, Huaiqu Feng, Shuo Kang, Jun Wang, Zhenbo Wei
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PROSAIL-Net: A transfer learning-based dual stream neural network to estimate leaf chlorophyll and leaf angle of crops from UAV hyperspectral images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-03 Sourav Bhadra, Vasit Sagan, Supria Sarkar, Maxwell Braud, Todd C. Mockler, Andrea L. Eveland
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WHU-Urban3D: An urban scene LiDAR point cloud dataset for semantic instance segmentation ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-02 Xu Han, Chong Liu, Yuzhou Zhou, Kai Tan, Zhen Dong, Bisheng Yang
With the rapid advancement of 3D sensors, there is an increasing demand for 3D scene understanding and an increasing number of 3D deep learning algorithms have been proposed. However, a large-scale and richly annotated 3D point cloud dataset is critical to understanding complicated road and urban scenes. Motivated by the need to bridge the gap between the rising demand for 3D urban scene understanding
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Estimating fractional vegetation cover from multispectral unmixing modeled with local endmember variability and spatial contextual information ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-01 Tianqi Zhang, Desheng Liu
Vegetation fractional cover (fCover) is an important canopy structural variable for understanding the climate-vegetation feedback. Trees and non-tree vegetation may respond differently to climate changes, yet traditional fCover estimation methods focus on quantifying fractional cover for general vegetation. Satellite-based spectral unmixing is more advantageous in this regard as it allows for trees
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A novel method for robust marine habitat mapping using a kernelised aquatic vegetation index ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-29 Stanley Mastrantonis, Ben Radford, Tim Langlois, Claude Spencer, Simon de Lestang, Sharyn Hickey
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Active fire-based dating accuracy for Landsat burned area maps is high in boreal and Mediterranean biomes and low in grasslands and savannas ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-28 Alana K. Neves, José M.C. Pereira, João M.N. Silva, Sílvia Catarino, Patricia Oliva, Emilio Chuvieco, Manuel L. Campagnolo
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The anisotropy of MODIS LST in urban areas: A perspective from different time scales using model simulations ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-28 Xiaoyu He, Dandan Wang, Si Gao, Xue Li, Gaijing Chang, Xiaodong Jia, Qiang Chen
Remote sensing is one of the effective means to obtain urban land surface temperature (LST), but the observed temperature varies with sensor viewing angle due to urban thermal anisotropy (UTA) and biased sensor viewing angle. The anisotropy of satellite-based LST products (e.g., MODIS LST) varies at different time scales. Previous researches focus on the anisotropy of MODIS LST at the seasonal scale
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Entropy-Based re-sampling method on SAR class imbalance target detection ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-28 Chong-Qi Zhang, Yao Deng, Ming-Zhe Chong, Zi-Wen Zhang, Yun-Hua Tan
Detection tasks based on Synthetic aperture radar (SAR) images have been studied widely but severely constrained by the quality of datasets. Meanwhile, both the unperceived category imbalance problem and SAR image discrepancy of multi-class SAR datasets are not fully considered. Researchers usually care about the foreground-background imbalance more than the class imbalance for SAR images. To solve
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Hazard or Non-Hazard Flood: Post Analysis for Paddy Rice, Wetland, and Other Potential Non-Hazard Flood Extraction from the VIIRS Flood Products ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-27 Donglian Sun, Tianshu Yang, Sanmei Li, Mitchell Goldberg, Satya Kalluri, Sean Helfrich, Bill Sjonberg, Lihang Zhou, Qingyuan Zhang, William Straka, Ruixin Yang, Fernando Miralles-Wilhelm
VIIRS flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) and the International Charter Program for rescue and relief efforts. However, some water bodies, like irrigated or flooded paddy rice fields, and water in seasonal wetlands, are detected as floodwater instead of permanent, seasonal, controlled
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A cluster-based disambiguation method using pose consistency verification for structure from motion ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-27 Ye Gong, Pengwei Zhou, Changfeng Liu, Yan Yu, Jian Yao, Wei Yuan, Li Li
Structure from motion (SfM) recovers scene structures and camera poses based on feature matching, and faces challenges from ambiguous scenes. There are a large number of ambiguous scenes in real environment, which contain many duplicate structures and textures. The ambiguity leads to incorrect feature matches between images with similar appearance, and makes geometric misalignment in SfM. To address
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Interannual changes of urban wetlands in China’s major cities from 1985 to 2022 ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-21 Ming Wang, Dehua Mao, Yeqiao Wang, Huiying Li, Jianing Zhen, Hengxing Xiang, Yongxing Ren, Mingming Jia, Kaishan Song, Zongming Wang
With global climate change and accelerating urbanization, accurate and timely extent information on urban wetlands is extremely important for sustainable urban development and conservation of ecosystem services, supporting the implementation and evaluation of the United Nations Sustainable Development Goals (SDGs). China has experienced the most dramatic urbanization process in recent decades, but
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Few-shot remote sensing image scene classification: Recent advances, new baselines, and future trends ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-20 Chunping Qiu, Xiaoyu Zhang, Xiaochong Tong, Naiyang Guan, Xiaodong Yi, Ke Yang, Junjie Zhu, Anzhu Yu
Remote sensing image scene classification (RSI-SC) is crucial for various high-level applications, including RSI retrieval, image captioning, and object detection. Deep learning-based methods can accurately predict scene categories. However, these approaches often require numerous labeled samples for training, limiting their practicality in real-world RS applications with scarce label resources. In
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Unrestricted region and scale: Deep self-supervised building mapping framework across different cities from five continents ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-19 Qiqi Zhu, Zhen Li, Tianjian Song, Ling Yao, Qingfeng Guan, Liangpei Zhang
Building footprint information is crucial for comprehending global urban development processes. Deep learning algorithms have shown significant potential in building extraction from high spatial resolution imagery. However, the requirement for large-scale annotated data has been a limitation for applying deep learning methods to city-level or national-level building mapping. The dynamic change and
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Supervised terrestrial to airborne laser scanner model calibration for 3D individual-tree attribute mapping using deep neural networks ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-19 Zhouxin Xi, Chris Hopkinson, Laura Chasmer
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Scale-aware deep reinforcement learning for high resolution remote sensing imagery classification ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-17 Yinhe Liu, Yanfei Zhong, Sunan Shi, Liangpei Zhang
Land-use/land-cover (LULC) classification of high spatial resolution (HSR) remote sensing imagery has been successfully improved using deep learning techniques. However, the current deep learning-based classification methods necessitate the division of remote sensing imagery into smaller and fixed image patches, primarily due to computational constraints arising from the extensive size of these images
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Multidecadal mapping of status and trends in annual burn probability over Canada’s forested ecosystems ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-17 Christopher Mulverhill, Nicholas C. Coops, Michael A. Wulder, Joanne C. White, Txomin Hermosilla, Christopher W. Bater
Globally, wildfires burn an average of approximately 5.5 Mha of forest per year. Deriving a detailed inventory of forest fuel conditions is critical to managing resources both before and during a fire. However, data products that form the basis of these inventories often come from disparate sources, may not be subject to update, or may not capture information relevant to fuels and fire behaviour. Satellite-derived
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Daily DeepCropNet: A hierarchical deep learning approach with daily time series of vegetation indices and climatic variables for corn yield estimation ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-17 Xingguo Xiong, Renhai Zhong, Qiyu Tian, Jingfeng Huang, Linchao Zhu, Yi Yang, Tao Lin
Accurate large-scale crop yield estimation under climate variability is essential to understanding the dynamics of global food security. The deep learning method has shown well performance for crop yield estimation because of its high capacity for temporal pattern recognition. However, most existing deep learning models were usually based on multi-source time series with weekly or coarse temporal resolutions
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LoveNAS: Towards multi-scene land-cover mapping via hierarchical searching adaptive network ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-17 Junjue Wang, Yanfei Zhong, Ailong Ma, Zhuo Zheng, Yuting Wan, Liangpei Zhang
Land-cover information reflects basic Earth’s surface environments and is critical to human settlements. As a well-established deep learning architecture, the fully convolutional network has achieved impressive progress in various land-cover mapping tasks. However, most research has focused on designing powerful encoders, ignoring the exploration of decoders. The existing handcrafted decoders are relatively
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Bamboo classification based on GEDI, time-series Sentinel-2 images and whale-optimized, dual-channel DenseNet: A case study in Zhejiang province, China ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-17 Bo Wang, Hong Zhao, Xiaoyi Wang, Guanting Lyu, Kuangmin Chen, Jinfeng Xu, Guishan Cui, Liheng Zhong, Le Yu, Huabing Huang, Qinghong Sheng
Regional carbon sink estimation and local forest management require spatially explicit maps of bamboo distribution. However, accurate bamboo mapping is challenging due to the similarity of bamboo’s optical spectral with those of other vegetation. To gain a high-precision bamboo forest distribution map circa year 2020, we developed a novel classification framework that integrated measurements, GEDI
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Superpixelwise likelihood ratio test statistic for PolSAR data and its application to built-up area extraction ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-14 Fan Zhang, Xuejiao Sun, Fei Ma, Qiang Yin
The natural terrain (e.g., farm and forest) in temperate zones changes dramatically between seasons due to distinct temperatures and precipitation variations from summer to winter. Moreover, built-up areas vary little in this short period. Therefore, extracting built-up areas via change detection on polarimetric synthetic aperture radar (PolSAR) images is feasible. A common type of PolSAR change detection
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From lines to Polygons: Polygonal building contour extraction from High-Resolution remote sensing imagery ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-14 Shiqing Wei, Tao Zhang, Dawen Yu, Shunping Ji, Yongjun Zhang, Jianya Gong
Automated extraction of polygonal building contours from high-resolution remote sensing images is important for various applications. However, it remains a difficult task to achieve automated extraction of polygonal buildings at the level of human delineation due to diverse building structures and imperfect image conditions. In this paper, we propose Line2Poly, an end-to-end approach that uses feature