样式: 排序: IF: - GO 导出 标记为已读
-
Scattering power components from dual-pol Sentinel-1 SLC and GRD SAR data ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-14 Abhinav Verma, Avik Bhattacharya, Subhadip Dey, Carlos López-Martínez, Paolo Gamba
Accurate land cover information is pivotal in numerous planning and management activities. Synthetic Aperture Radar (SAR) data has emerged as a valuable resource for land cover assessment. Extracting scattering power components from Polarimetric SAR (PolSAR) data provides essential insights into the characteristics of land cover targets, aiding in their detailed characterization. Various techniques
-
Deep learning-based harmonization and super-resolution of Landsat-8 and Sentinel-2 images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-14 Venkatesh Thirugnana Sambandham, Konstantin Kirchheim, Frank Ortmeier, Sayan Mukhopadhaya
Multi-spectral satellite images of the Earth’s surface are used in various applications, from water quality assessment and urban planning to climate monitoring, disaster response, infrastructure oversight, and agricultural surveillance. Many of these applications would benefit from higher spatial and temporal resolution of observations, which could be achieved by combining observations from several
-
Rapid in-flight image quality check for UAV-enabled bridge inspection ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-13 Feng Wang, Yang Zou, Xiaoyu Chen, Cheng Zhang, Lei Hou, Enrique del Rey Castillo, James B.P. Lim
Combining Unmanned Aerial Vehicles (UAVs) and close-range photogrammetry has become a safer, more efficient, and cost-effective solution for bridge inspection compared to conventional methods. However, close-range bridge images captured by UAVs often suffer from severe quality issues, such as blurriness, improper exposure, limited coverage, or insufficient resolution. These issues can adversely affect
-
Continent-wide urban tree canopy fine-scale mapping and coverage assessment in South America with high-resolution satellite images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-13 Jianhua Guo, Danfeng Hong, Zhiheng Liu, Xiao Xiang Zhu
-
National-scale nighttime high-temperature anomalies from Landsat-8 OLI images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-10 Huansha Wu, Yongxue Liu, Yulin Pu, Peng Liu, Wanjing Zhao, Xiaoxiao Guo
-
Combining deep learning with physical parameters in POC and PIC inversion from spaceborne lidar CALIOP ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-09 Zhenhua Zhang, Siqi Zhang, Michael J. Behrenfeld, Peng Chen, Cédric Jamet, Paolo Di Girolamo, Davide Dionisi, Yongxiang Hu, Xiaomei Lu, Yuliang Pan, Minzhe Luo, Haiqing Huang, Delu Pan
POC and PIC are indispensable components in the global ocean carbon cycle, their transport and space distribution being driven by the biological carbon pump and the carbonate pump. However, passive ocean color remote sensing, usually employed for POC and PIC research, experiences serious shortcomings in polar winter conditions due to its reliance on sunlight, leading to scarce data coverage in the
-
Chat3D: Interactive understanding 3D scene-level point clouds by chatting with foundation model for urban ecological construction ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-09 Yiping Chen, Shuai Zhang, Ting Han, Yumeng Du, Wuming Zhang, Jonathan Li
With the artificial intelligence technology development boom, large language models are demonstrating their potential in comprehension and creativity. Large language models such as GPT-4 and Gemini have been able to powerfully study for various professional-level exams. However, as a language model itself, its powerful comprehension can only be reflected in text sequences. Currently, although videos
-
Effect of intra-year Landsat scene availability in land cover land use classification in the conterminous United States using deep neural networks ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-07 Giorgos Mountrakis, Shahriar S. Heydari
-
Towards multi-views cloud retrieval accounting for the 3-D structure collected by directional polarization camera ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-06 Haixiao Yu, Xiaobing Sun, Bihai Tu, Rufang Ti, Jinji Ma, Jin Hong, Cheng Chen, Xiao Liu, Honglian Huang, Zeling Wang, Safura Ahmad, Yi Wang, Yizhe Fan, Yiqi Li, Yichen Wei, Yuxuan Wang, Yuyao Wang
The structure of a cloud is three-dimensional (3-D). Nevertheless, cloud research is predominantly developed in one-dimensional (1-D) hypothesis from passive optical satellite measurements. Cloud retrieval algorithms typically do not consider the parallax displacement of clouds in multi-view images. Inversion of cloud properties based on two-dimensional (2-D) coordinates cannot solve multi-view radiation
-
Neural implicit shape modeling for small planetary bodies from multi-view images using a mask-based classification sampling strategy ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-05 Hao Chen, Xuanyu Hu, Konrad Willner, Zhen Ye, Friedrich Damme, Philipp Gläser, Yongjie Zheng, Xiaohua Tong, Hauke Hußmann, Jürgen Oberst
-
Forest feature LiDAR SLAM (F2-LSLAM) for backpack systems ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-04 Tian Zhou, Chunxi Zhao, Cameron Patrick Wingren, Songlin Fei, Ayman Habib
-
Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-04 Xianghong Che, Hankui K. Zhang, Zhongbin B. Li, Yong Wang, Qing Sun, Dong Luo, Hao Wang
Satellite time series data, widely used for land cover classification, often contain missing values due to cloud contamination, which can negatively affect classification. Numerous strategies have been developed to reconstruct the missing values to produce regular time series for machine learning classifiers, among which the compositing followed by the linear interpolation is most widely used. However
-
Lightweight and rotation-invariant place recognition network for large-scale raw point clouds ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-03 Zhenghua Zhang, Hu Liu, Xuan Wang, Mingcong Shu, Guoliang Chen, Qiuzhao Zhang
LiDAR-based place recognition is crucial for Simultaneous Localization and Mapping (SLAM) and autonomous driving. However, existing methods face challenges in achieving rotation invariance and are hindered by a large number of parameters and complex data preprocessing steps, such as ground point removal and extensive down-sampling. These limitations hinder their practical implementation in complex
-
A feature selection method for multimodal multispectral LiDAR sensing ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-03 Yu Han, David Salido-Monzú, Jemil Avers Butt, Sebastian Schweizer, Andreas Wieser
-
Adversarial learning-based camera pose-to-image mapping network for synthesizing new view in real indoor environments ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-02 Xujie Kang, Kangling Liu, Jiang Duan, Yuanhao Gong, Guoping Qiu
-
Decouple and weight semi-supervised semantic segmentation of remote sensing images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-05-02 Wei Huang, Yilei Shi, Zhitong Xiong, Xiao Xiang Zhu
-
GlobalMind: Global multi-head interactive self-attention network for hyperspectral change detection ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-30 Meiqi Hu, Chen Wu, Liangpei Zhang
-
Towards real-time processing for UAV-mounted GPR-SAR imaging systems ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-27 María García-Fernández, Guillermo Álvarez-Narciandi, Jaime Laviada, Yuri Álvarez López, Fernando Las-Heras
-
Bundle adjustment with motion constraints for uncalibrated multi-camera systems at the ground level ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-26 Debao Huang, Rongjun Qin, Mostafa Elhashash
Multi-camera systems for structure from motion (SfM) are widely deployed in many mapping applications. Existing solutions assume known rig calibration, synchronized frames among cameras, as well as overlapping field of views (FoVs). In this paper, we derive novel geometric constraints assuming minimal knowns about the multi-camera systems, to benefit low-cost and non-expert use cases where uncalibrated
-
APC2Mesh: Bridging the gap from occluded building façades to full 3D models ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-25 Perpetual Hope Akwensi, Akshay Bharadwaj, Ruisheng Wang
The benefits of having digital twins of urban buildings are numerous. However, a major difficulty encountered in their creation from airborne LiDAR point clouds is the effective means of accurately reconstructing significant occlusions amidst point density variations and noise. To bridge the noise/sparsity/occlusion gap and generate high fidelity 3D building models, we propose APC2Mesh which integrates
-
PLANES4LOD2: Reconstruction of LoD-2 building models using a depth attention-based fully convolutional neural network ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-24 Philipp Schuegraf, Jie Shan, Ksenia Bittner
Level of detail (LoD)-2 reconstruction is an inevitable task in digital twin-related applications such as disaster management, flood simulation, landslide simulation and solar panel recommendation. However, there is a lack of capable methods that can exploit fine details in RGB imagery and mitigate noise in photogrammetric digital surface models (DSMs). Our investigation is focused on the use of roof
-
TemPanSharpening: A multi-temporal Pansharpening solution based on deep learning and edge extraction ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-04-23 Yifei Han, Hong Chi, Jinliang Huang, Xinyi Gao, Zhiyu Zhang, Feng Ling
The tradeoff among spatial, temporal, and spectral resolution of remote sensing (RS) images due to sensor properties limits the development of RS applications. Most image enhancement studies tend to focus on either spatio-temporal fusion or spatio-spectral fusion. As a more comprehensive solution, spatial–temporal-spectral fusion (STSF) is complicated but its potential is worth to be further explored
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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)
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
Changes in the Team of Associate Editors ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-03-26 Qihao Weng, Clément Mallet
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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