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Homogeneous tokenizer matters: Homogeneous visual tokenizer for remote sensing image understanding ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-21 Run Shao, Zhaoyang Zhang, Chao Tao, Yunsheng Zhang, Chengli Peng, Haifeng Li
On the basis of the transformer architecture and the pretext task of “next-token prediction”, multimodal large language models (MLLMs) are revolutionizing the paradigm in the field of remote sensing image understanding. However, the tokenizer, as one of the fundamental components of MLLMs, has long been overlooked or even misunderstood in visual tasks. A key factor contributing to the great comprehension
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A cost-effective and robust mapping method for diverse crop types using weakly supervised semantic segmentation with sparse point samples ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-20 Zhiwen Cai, Baodong Xu, Qiangyi Yu, Xinyu Zhang, Jingya Yang, Haodong Wei, Shiqi Li, Qian Song, Hang Xiong, Hao Wu, Wenbin Wu, Zhihua Shi, Qiong Hu
Accurate and timely information on the spatial distribution and areas of crop types is critical for yield estimation, agricultural management, and sustainable development. However, traditional crop classification methods often struggle to identify various crop types effectively due to their intricate spatiotemporal patterns and high training data demands. To address this challenge, we developed a Structure-aware
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SoftFormer: SAR-optical fusion transformer for urban land use and land cover classification ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-20 Rui Liu, Jing Ling, Hongsheng Zhang
Classification of urban land use and land cover is vital to many applications, and naturally becomes a popular topic in remote sensing. The finite information carried by unimodal data, the compound land use types, and the poor signal-noise ratio caused by restricted weather conditions would inevitably lead to relatively poor classification performance. Recently in remote sensing society, multimodal
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Nonlinear least-squares solutions to the TLS multi-station registration adjustment problem ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-19 Yu Hu, Xing Fang, Wenxian Zeng
Performing multiple scans is necessary to cover an entire scene of interest, making multi-station registration adjustment a critical task in terrestrial laser scanner data processing. Existing methods either rely on pair-wise adjustment, which leads to drift accumulation and lacks global consistency, or provide an approximate solution based on a linearized model, sacrificing statistical optimality
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A thin cloud blind correction method coupling a physical model with unsupervised deep learning for remote sensing imagery ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-19 Liying Xu, Huifang Li, Huanfeng Shen, Chi Zhang, Liangpei Zhang
Thin cloud disturbs the observation of optical sensors, thus reducing the quality of optical remote sensing images and limiting the subsequent applications. However, the reliance of the existing thin cloud correction methods on the assistance of in-situ parameters, prior assumptions, massive paired data, or special bands severely limits their generalization. Moreover, due to the inadequate consideration
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An automatic procedure for mapping burned areas globally using Sentinel-2 and VIIRS/MODIS active fires in Google Earth Engine ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-19 Aitor Bastarrika, Armando Rodriguez-Montellano, Ekhi Roteta, Stijn Hantson, Magí Franquesa, Leyre Torre, Jon Gonzalez-Ibarzabal, Karmele Artano, Pilar Martinez-Blanco, Amaia Mesanza, Jesús A. Anaya, Emilio Chuvieco
Understanding the spatial and temporal trends of burned areas (BA) on a global scale offers a comprehensive view of the underlying mechanisms driving fire incidence and its influence on ecosystems and vegetation recovery patterns over extended periods. Such insights are invaluable for modeling fire emissions and the formulation of strategies for post-fire rehabilitation planning.
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Weather-aware autopilot: Domain generalization for point cloud semantic segmentation in diverse weather scenarios ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-17 Jing Du, John Zelek, Jonathan Li
3D point cloud semantic segmentation, a pivotal task in fields such as autonomous driving and urban planning, confronts the challenge of performance degradation under adverse weather conditions. Current methodologies primarily focus on optimal weather scenarios, leaving a significant gap in handling various environmental adversities like fog, rain, and snow. To bridge this gap, we propose a comprehensive
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Joint block adjustment and variational optimization for global and local radiometric normalization toward multiple remote sensing image mosaicking ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-17 Dekun Lin, Huanfeng Shen, Xinghua Li, Chao Zeng, Tao Jiang, Yongming Ma, Mingjie Xu
Multi-temporal optical remote sensing images acquired from cross-sensor platforms often show significant radiometric differences, posing challenges when mosaicking images. These challenges include inconsistent global radiometric tones, unsmooth local radiometric transitions, and visible seamlines. In this paper, to address these challenges, we propose a two-stage approach for global and local radiometric
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Reconstructing NDVI time series in cloud-prone regions: A fusion-and-fit approach with deep learning residual constraint ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-16 Peng Qin, Huabing Huang, Peimin Chen, Hailong Tang, Jie Wang, Shuang Chen
The time series data of Normalized Difference Vegetation Index (NDVI) is crucial for monitoring changes in terrestrial vegetation. Existing reconstruction methods encounter challenges in areas prone to clouds, primarily due to inadequate utilization of spatial, temporal, periodic, and multi-sensor information, as well as a lack of physical interpretations. This frequently results in limited model performance
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MuSRFM: Multiple scale resolution fusion based precise and robust satellite derived bathymetry model for island nearshore shallow water regions using sentinel-2 multi-spectral imagery ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-14 Xiaoming Qin, Ziyin Wu, Xiaowen Luo, Jihong Shang, Dineng Zhao, Jieqiong Zhou, Jiaxin Cui, Hongyang Wan, Guochang Xu
The multi-spectral imagery based Satellite Derived Bathymetry (SDB) provides an efficient and cost-effective approach for acquiring bathymetry data of nearshore shallow water regions. Compared with conventional pixelwise inversion models, Deep Learning (DL) models have the theoretical capability to encompass a broader receptive field, automatically extracting comprehensive spatial features. However
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Snow depth retrieval method for PolSAR data using multi-parameters snow backscattering model ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-13 Haiwei Qiao, Ping Zhang, Zhen Li, Lei Huang, Zhipeng Wu, Shuo Gao, Chang Liu, Shuang Liang, Jianmin Zhou, Wei Sun
Snow depth (SD) is a crucial property of snow, its spatial and temporal variation is important for global change, snowmelt runoff simulation, disaster prediction, and freshwater storage estimation. Polarimetric Synthetic Aperture Radar (PolSAR) can precisely describe the backscattering of the target and emerge as an effective tool for SD retrieval. The backscattering component of dry snow is mainly
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A general albedo recovery approach for aerial photogrammetric images through inverse rendering ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-12 Shuang Song, Rongjun Qin
Modeling outdoor scenes for the synthetic 3D environment requires the recovery of reflectance/albedo information from raw images, which is an ill-posed problem due to the complicated unmodeled physics in this process (e.g., indirect lighting, volume scattering, specular reflection). The problem remains unsolved in a practical context. The recovered albedo can facilitate model relighting and shading
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Estimating AVHRR snow cover fraction by coupling physical constraints into a deep learning framework ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-12 Qin Zhao, Xiaohua Hao, Tao Che, Donghang Shao, Wenzheng Ji, Siqiong Luo, Guanghui Huang, Tianwen Feng, Leilei Dong, Xingliang Sun, Hongyi Li, Jian Wang
Accurate snow cover information is crucial for studying global climate and hydrology. Although deep learning has innovated snow cover fraction (SCF) retrieval, its effectiveness in practical application remains limited. This limitation stems from its reliance on appropriate training data and the necessity for more advanced interpretability. To overcome these challenges, a novel deep learning framework
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Sequential polarimetric phase optimization algorithm for dynamic deformation monitoring of landslides ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-12 Yian Wang, Jiayin Luo, Jie Dong, Jordi J. Mallorqui, Mingsheng Liao, Lu Zhang, Jianya Gong
In the era of big SAR data, it is urgent to develop dynamic time series DInSAR processing procedures for near-real-time monitoring of landslides. However, the dense vegetation coverage in mountainous areas causes severe decorrelations, which demands high precision and efficiency of phase optimization processing. The common phase optimization using single-polarization SAR data cannot produce satisfactory
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High-resolution mapping of grassland canopy cover in China through the integration of extensive drone imagery and satellite data ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-11 Tianyu Hu, Mengqi Cao, Xiaoxia Zhao, Xiaoqiang Liu, Zhonghua Liu, Liangyun Liu, Zhenying Huang, Shengli Tao, Zhiyao Tang, Yanpei Guo, Chengjun Ji, Chengyang Zheng, Guoyan Wang, Xiaokang Hu, Luhong Zhou, Yunxiang Cheng, Wenhong Ma, Yonghui Wang, Pujin Zhang, Yuejun Fan, Feihai Yu, Zhong Wang, Xiujuan Qiao, Xiaoli Cheng, Chunying Yin, Hongyuan Ma, Liping Li, Yan Yang, Wanyin Luo, Yanming Gong, Lei Wang
Canopy cover is a crucial indicator for assessing grassland health and ecosystem services. However, achieving accurate high-resolution estimates of grassland canopy cover at a large spatial scale remains challenging due to the limited spatial coverage of field measurements and the scale mismatch between field measurements and satellite imagery. In this study, we addressed these challenges by proposing
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Effective variance attention-enhanced diffusion model for crop field aerial image super resolution ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-11 Xiangyu Lu, Jianlin Zhang, Rui Yang, Qina Yang, Mengyuan Chen, Hongxing Xu, Pinjun Wan, Jiawen Guo, Fei Liu
Image super-resolution (SR) can significantly improve the resolution and quality of aerial imagery. Emerging diffusion models (DM) have shown superior image generation capabilities through multistep refinement. To explore their effectiveness on high-resolution cropland aerial imagery SR, we first built the CropSR dataset, which includes 321,992 samples for self-supervised SR training and two real-matched
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Harmony in diversity: Content cleansing change detection framework for very-high-resolution remote-sensing images ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-10 Mofan Cheng, Wei He, Zhuohong Li, Guangyi Yang, Hongyan Zhang
Change detection, as a crucial task in the field of Earth observation, aims to identify changed pixels between multi-temporal remote-sensing images captured at the same geographical area. However, in practical applications, there are challenges of pseudo changes arising from diverse imaging conditions and different remote-sensing platforms. Existing methods either overlook the different imaging styles
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Review of synthetic aperture radar with deep learning in agricultural applications ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-09-10 Mahya G.Z. Hashemi, Ehsan Jalilvand, Hamed Alemohammad, Pang-Ning Tan, Narendra N. Das
Synthetic Aperture Radar (SAR) observations, valued for their consistent acquisition schedule and not being affected by cloud cover and variations between day and night, have become extensively utilized in a range of agricultural applications. The advent of deep learning allows for the capture of salient features from SAR observations. This is accomplished through discerning both spatial and temporal
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Towards SDG 11: Large-scale geographic and demographic characterisation of informal settlements fusing remote sensing, POI, and open geo-data ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-31 Wei Tu, Dongsheng Chen, Rui Cao, Jizhe Xia, Yatao Zhang, Qingquan Li
Informal settlements’ geographic and demographic mapping is essential for evaluating human-centric sustainable development in cities, thus fostering the road to Sustainable Development Goal 11. However, fine-grained informal settlements’ geographic and demographic information is not well available. To fill the gap, this study proposes an effective framework for both fine-grained geographic and demographic
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A spatiotemporal shape model fitting method for within-season crop phenology detection ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-30 Ruyin Cao, Luchun Li, Licong Liu, Hongyi Liang, Xiaolin Zhu, Miaogen Shen, Ji Zhou, Yuechen Li, Jin Chen
Crop phenological information must be reliably acquired earlier in the growing season to benefit agricultural management. Although the popular shape model fitting (SMF) method and its various improved versions (e.g., SMF by the Separate phenological stage, SMF-S) have been successfully applied to after-season crop phenology detection, these existing methods cannot be applied to within-season crop phenology
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Annual improved maps to understand the complete evolution of 9 thousand lakes on the Tibetan plateau in 1991–2023 ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-29 Yan Zhou, Bailu Liu, Yaoping Cui, Xinxin Wang, Mengmeng Cao, Sen Zhang, Xiangming Xiao, Jinwei Dong
Rapid changes in the densely distributed lakes on the Tibetan Plateau (TP) reflect the responses of terrestrial water resources to climate change. Timely and accurate monitoring of lake dynamics is essential for formulating adaptation strategies to manage water and protect public facility safety sustainably. Interfered by the numerous glaciers and snow mountains and limited by the acquisition and computing
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Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-29 Zheng Gong, Wenyan Ge, Jiaqi Guo, Jincheng Liu
Vegetation phenology serves as a crucial indicator of ecosystem dynamics and its response to environmental cues. Against the backdrop of global climate warming, it plays a pivotal role in delving into global climate change, terrestrial ecosystem dynamics, and guiding agricultural production. Ground-based field observations of vegetation phenology are increasingly challenged by rapid global ecological
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Tracking paddy rice acreage, flooding impacts, and mitigations during El Niño flooding events using Sentinel-1/2 imagery and cloud computing ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-29 Ruoqi Liu, Jinwei Dong, Yong Ge, Hui Lin, Xianghong Che, Yuanyuan Di, Xi Chen, Shuhua Qi, Mingjun Ding, Xiangming Xiao, Geli Zhang
The frequent occurrence of El Niño events, in the context of climate change, brings heavy precipitation and extreme heat, severely disrupting agricultural production. Previous efforts have focused on monitoring crop planting areas and evaluating affected crops during disasters. Nevertheless, a comprehensive analysis, including crop planting area mapping, crop damage assessment, and mitigation effectiveness
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A novel soybean mapping index within the global optimal time window ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-28 Guilong Xiao, Jianxi Huang, Jianjian Song, Xuecao Li, Kaiqi Du, Hai Huang, Wei Su, Shuangxi Miao
Efficient soybean mapping is critical for agricultural production and yield prediction. However, current sample-driven soybean mapping methods heavily rely on large representative sample datasets, limiting the interpretability of physical mechanisms. Besides, sample-free methods failed to exploit key features that differentiate soybean from other crops, especially Chlorophyll content. Misclassification
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Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-24 Xiaohu Lin, Xin Yang, Wanqiang Yao, Xiqi Wang, Xiongwei Ma, Bolin Ma
Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based
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CadastreVision: A benchmark dataset for cadastral boundary delineation from multi-resolution earth observation images ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-24 Jeroen Grift, Claudio Persello, Mila Koeva
Approximately 70%–75% of people worldwide have no formally registered land rights. Fit-For-Purpose Land Administration was introduced to address this problem and focuses on delineating visible cadastral boundaries from earth observation imagery. Recent studies have shown the potential of deep learning models to extract these visible cadastral boundaries automatically. However, studies are limited by
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UB-FineNet: Urban building fine-grained classification network for open-access satellite images ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-23 Zhiyi He, Wei Yao, Jie Shao, Puzuo Wang
Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban
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Integrating geographic knowledge into deep learning for spatiotemporal local climate zone mapping derived thermal environment exploration across Chinese climate zones ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-21 Qiqi Zhu, Longli Ran, Yunchang Zhang, Qingfeng Guan
The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However
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Seasonally inundated area extraction based on long time-series surface water dynamics for improved flood mapping ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-20 Bingyu Zhao, Jianjun Wu, Meng Chen, Jingyu Lin, Ruohua Du
Accurate extraction of Seasonally Inundated Area (SIA) is pivotal for precise delineation of Flood Inundation Area (FIA). Current methods predominantly rely on Water Inundation Frequency (WIF) to extract SIA, which, due to the lack of analysis of dynamic surface water changes, often yields less accurate and robust results. This significantly hampers the rapid and precise mapping of FIA. In the study
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Sub-national scale mapping of individual olive trees integrating Earth observation and deep learning ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-19 Chenxi Lin, Junxiong Zhou, Leikun Yin, Rachid Bouabid, David Mulla, Elinor Benami, Zhenong Jin
The olive tree holds great cultural, environmental, and economic significance in the Mediterranean region. In particular, Morocco has been making dedicated investments over $10 billion since 2008 to fuel the transition from cereal to olive production. Understanding the spatial extent of this large-scale land conversion is critical for a variety of socioeconomic purposes. In response to this demand
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Coarse-to-fine semantic segmentation of satellite images ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-16 Hao Chen, Wen Yang, Li Liu, Gui-Song Xia
Training deep neural networks for semantic segmentation of aerial images relies heavily on obtaining a large number of precise pixel-level annotations, which can cause significant annotation expenses. Given the fact that acquiring fine-class annotations is considerably more challenging than obtaining coarse-class annotations, we present a novel semi-supervised learning framework, which utilizes high
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From satellite-based phenological metrics to crop planting dates: Deriving field-level planting dates for corn and soybean in the U.S. Midwest ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-16 Qu Zhou, Kaiyu Guan, Sheng Wang, James Hipple, Zhangliang Chen
Information on planting dates is crucial for modeling crop development, analyzing crop yield, and evaluating the effectiveness of policy-driven planting windows. Despite their high importance, field-level planting date datasets are scarce. Satellite remote sensing provides accurate and cost-effective solutions for detecting crop phenology from moderate to high resolutions, but remote sensing-based
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Unveiling spatiotemporal tree cover patterns in China: The first 30 m annual tree cover mapping from 1985 to 2023 ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-13 Yaotong Cai, Xiaocong Xu, Peng Zhu, Sheng Nie, Cheng Wang, Yujiu Xiong, Xiaoping Liu
China leads in the greening of the world, with a nearly doubled increase in its forest area since the 1980 s revealed by the National Forest Inventory (NFI). However, a significant challenge persists in the absence of consistent and reliable remote sensing data that align with the NFI, hindering a comprehensive understanding of the spatiotemporal patterns of terrestrial ecosystem changes driven by
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Fraction-dependent variations in cooling efficiency of urban trees across global cities ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-09 Wenfeng Zhan, Chunli Wang, Shasha Wang, Long Li, Yingying Ji, Huilin Du, Fan Huang, Sida Jiang, Zihan Liu, Huyan Fu
Investigating the relationship between cooling efficiency (CE) and tree cover percentage (TCP) is critical for planning of green space within cities. However, the spatiotemporal complexities of the intra-city CE-TCP relationship worldwide with distinct climates, as well as the differing impacts of consistently increasing tree cover within urban regions on cooling potential, remain unclear. Here we
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Semi-supervised multi-class tree crown delineation using aerial multispectral imagery and lidar data ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-08 S. Dersch, A. Schöttl, P. Krzystek, M. Heurich
The segmentation of individual trees based on deep learning is more accurate than conventional meth- ods. However, a sufficient amount of training data is mandatory to leverage the accuracy potential of deep learning-based approaches. Semi-supervised learning techniques, by contrast, can help simplify the time-consuming labelling process. In this study, we introduce a new semi-supervised tree segmen-
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Bark beetle pre-emergence detection using multi-temporal hyperspectral drone images: Green shoulder indices can indicate subtle tree vitality decline ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-08 Langning Huo, Niko Koivumäki, Raquel A. Oliveira, Teemu Hakala, Lauri Markelin, Roope Näsi, Juha Suomalainen, Antti Polvivaara, Samuli Junttila, Eija Honkavaara
Forest stress monitoring and in-time identification of forest disturbances are important to improve forest resilience to climate change. Fast-developing drone techniques and hyperspectral imagery provide tools for understanding the forest decline process under stress and contribute to focused monitoring. This study explored and developed hyperspectral drone imagery for early detection of forest stress
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TSG-Seg: Temporal-selective guidance for semi-supervised semantic segmentation of 3D LiDAR point clouds ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-08 Weihao Xuan, Heli Qi, Aoran Xiao
LiDAR-based semantic scene understanding holds a pivotal role in various applications, including remote sensing and autonomous driving. However, the majority of LiDAR segmentation models rely on extensive and densely annotated training datasets, which is extremely laborious to annotate and hinder the widespread adoption of LiDAR systems. Semi-supervised learning (SSL) offers a promising solution by
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Training-free thick cloud removal for Sentinel-2 imagery using value propagation interpolation ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-07 Laurens Arp, Holger Hoos, Peter van Bodegom, Alistair Francis, James Wheeler, Dean van Laar, Mitra Baratchi
Remote sensing imagery has an ever-increasing impact on important downstream applications, such as vegetation monitoring and climate change modelling. Clouds obscuring parts of the images create a substantial bottleneck in most machine learning tasks that use remote sensing data, and being robust to this issue is an important technical challenge. In many cases, cloudy images cannot be used in a machine
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The importance of spatial scale and vegetation complexity in woody species diversity and its relationship with remotely sensed variables ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-07 Wendy G. Canto-Sansores, Jorge Omar López-Martínez, Edgar J. González, Jorge A. Meave, José Luis Hernández-Stefanoni, Pedro A. Macario-Mendoza
Plant species diversity is key to ecosystem functioning, but in recent decades anthropogenic activities have prompted an alarming decline in this community trait. Thus, developing strategies to understand diversity dynamics based on affordable and efficient remote sensing monitoring is essential, as well as examining the relevance of spatial scale and vegetation structural complexity to these dynamics
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Beyond clouds: Seamless flood mapping using Harmonized Landsat and Sentinel-2 time series imagery and water occurrence data ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-07 Zhiwei Li, Shaofen Xu, Qihao Weng
Floods are among the most devastating natural disasters, posing significant risks to life, property, and infrastructure globally. Earth observation satellites provide data for continuous and extensive flood monitoring, yet limitations exist in the spatial completeness of monitoring using optical images due to cloud cover. Recent studies have developed gap-filling methods for reconstructing cloud-covered
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Temporal-spectral-semantic-aware convolutional transformer network for multi-class tidal wetland change detection in Greater Bay Area ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-06 Siyu Qian, Zhaohui Xue, Mingming Jia, Yiping Chen, Hongjun Su
Coastal tidal wetlands are crucial for environmental and economic health, but facing threats from various environmental changes. Detecting changes of tidal wetlands is essential for promoting sustainable development in coastal areas. Despite extensive researches on tidal wetland changes, persistent challenges still exist. Firstly, the high similarity among tidal wetland types hinders the effectiveness
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Modeling the top-of-atmosphere radiance of alpine snow with topographic effects explicitly solved ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-03 Gongxue Wang, Lingmei Jiang, Fangbo Pan, Huizhen Cui, Shuhua Zhang
Optical remote sensing of snow is challenged by the complex radiative transfer mechanism in alpine environments. The representation of topographic effects in interpreting satellite imagery of snow is still limited to inadequate analytical modelization. Here we develop a framework that explicitly solves multiple terrain reflections and generates the top-of-atmosphere (TOA) radiance of alpine snow by
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A framework for fully automated reconstruction of semantic building model at urban-scale using textured LoD2 data ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-01 Yuefeng Wang, Wei Jiao, Hongchao Fan, Guoqing Zhou
The CityGML Level of Detail 3 (LoD3), a widely adopted standard for three-dimensional (3D) city modeling, has been accessible for an extended period. However, its comprehensive implementation remains limited due to challenges such as insufficient automation and inconsistent data quality. This research introduces an innovative and fully automated framework aimed at urban-scale semantic building model
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CropSight: Towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and PlanetScope satellite imagery ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-08-01 Yin Liu, Chunyuan Diao, Weiye Mei, Chishan Zhang
Crop type maps are essential in informing agricultural policy decisions by providing crucial data on the specific crops cultivated in given regions. The generation of crop type maps usually involves the collection of ground truth data of various crop species, which can be challenging at large scales. As an alternative to conventional field observations, street view images offer a valuable and extensive
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Catadioptric omnidirectional thermal odometry in dynamic environment ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-31 Yuzhen Wu, Lingxue Wang, Lian Zhang, Xudong Han, Dezhi Zheng, Shuigen Wang, Yanqiu Li, Yi Cai
This paper presents a catadioptric omnidirectional thermal odometry (COTO) system that estimates the six degrees of freedom (DoF) pose of a camera using only omnidirectional thermal images in visually degraded, fast-motion, and dynamic environments. First, we design and fabricate a central hyperbolic catadioptric omnidirectional thermal camera that captures surrounding thermal images with horizontal
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In-orbit detection of the spectral smile for the Mars Mineral Spectrometer ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-30 Bing Wu, Rui Xu, Chengyu Liu, Zhiping He
As a payload of Tianwen-1 (TW-1), the Mars Mineral Spectrometer (MMS) is tasked with acquiring hyperspectral data of the Martian surface to detect material composition. Microdeformations in optical, mechanical, and thermal components result in the MMS experiencing spectral response distortion in orbit, leading to systematic changes in pixel central wavelengths and full width at half maximum (FWHM)
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Simulated SAR prior knowledge guided evidential deep learning for reliable few-shot SAR target recognition ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-30 Xiaoyan Zhou, Tao Tang, Qishan He, Lingjun Zhao, Gangyao Kuang, Li Liu
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) plays a pivotal role in civilian and military applications. However, the limited labeled samples present a significant challenge in deep learning-based SAR ATR. Few-shot learning (FSL) offers a potential solution, but models trained with limited samples may produce a high probability of incorrect results that can mislead decision-makers
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Adaptive variational decomposition for water-related optical image enhancement ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-30 Jingchun Zhou, Shuhan Chen, Dehuan Zhang, Zongxin He, Kin-Man Lam, Ferdous Sohel, Gemine Vivone
Underwater images suffer from blurred details and color distortion due to light attenuation from scattering and absorption. Current underwater image enhancement (UIE) methods overlook the effects of forward scattering, leading to difficulties in addressing low contrast and blurriness. To address the challenges caused by forward and backward scattering, we propose a novel variational-based adaptive
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Towards a gapless 1 km fractional snow cover via a data fusion framework ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-29 Xiongxin Xiao, Tao He, Shuang Liang, Shunlin Liang, Xinyan Liu, Yichuan Ma, Jun Wan
Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth’s energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor
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PriNeRF: Prior constrained Neural Radiance Field for robust novel view synthesis of urban scenes with fewer views ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-29 Kaiqiang Chen, Bo Dong, Zhirui Wang, Peirui Cheng, Menglong Yan, Xian Sun, Michael Weinmann, Martin Weinmann
Novel view synthesis (NVS) of urban scenes enables the exploration of cities virtually and interactively, which can further be used for urban planning, navigation, digital tourism, etc. However, many current NVS methods require a large amount of images from known views as input and are sensitive to intrinsic and extrinsic camera parameters. In this paper, we propose a new unified framework for NVS
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A semi-supervised multi-temporal landslide and flash flood event detection methodology for unexplored regions using massive satellite image time series ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-29 Axel A.J. Deijns, David Michéa, Aline Déprez, Jean-Philippe Malet, François Kervyn, Wim Thiery, Olivier Dewitte
Landslides and flash floods are geomorphic hazards (GH) that often co-occur and interact and frequently lead to societal and environmental impact. The compilation of detailed multi-temporal inventories of GH events over a variety of contrasting natural as well as human-influenced landscapes is essential to understanding their behavior in both space and time and allows to unravel the human drivers from
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Coherence bias mitigation through regularized tapered coherence matrix for phase linking in decorrelated environments ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-27 Hongyu Liang, Lei Zhang, Xin Li, Jicang Wu
Phase linking technique has shown the ability to mitigate the decorrelation effect on the time series interferometric synthetic aperture radar (InSAR) data. By imposing the temporal phase-closure constraint, this technique reconstructs a consistent phase series from the complex sample coherence matrix (SCM). However, the bias of coherence estimates degrades the performance of phase linking, especially
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Point2Building: Reconstructing buildings from airborne LiDAR point clouds ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-26 Yujia Liu, Anton Obukhov, Jan Dirk Wegner, Konrad Schindler
We present a learning-based approach to reconstructing buildings as 3D polygonal meshes from airborne LiDAR point clouds. What makes 3D building reconstruction from airborne LiDAR difficult is the large diversity of building designs, especially roof shapes, the low and varying point density across the scene, and the often incomplete coverage of building facades due to occlusions by vegetation or the
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Gap completion in point cloud scene occluded by vehicles using SGC-Net ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-23 Yu Feng, Yiming Xu, Yan Xia, Claus Brenner, Monika Sester
Recent advances in mobile mapping systems have greatly enhanced the efficiency and convenience of acquiring urban 3D data. These systems utilize LiDAR sensors mounted on vehicles to capture vast cityscapes. However, a significant challenge arises due to occlusions caused by roadside parked vehicles, leading to the loss of scene information, particularly on the roads, sidewalks, curbs, and the lower
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Incremental multi temporal InSAR analysis via recursive sequential estimator for long-term landslide deformation monitoring ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-19 Meng Ao, Lianhuan Wei, Mingsheng Liao, Lu Zhang, Jie Dong, Shanjun Liu
Distributed Scatterers Interferometry (DS-InSAR) has been widely applied to increase the number of measurement points (MP) in complex mountainous areas with dense vegetation and complicated topography. However, DS-InSAR method adopts batch processing mode. When new observation data acquired, the entire archived data is reprocessed, completely ignoring the existing results, and not suitable for high-performance
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Quality control tests for automated above-water hyperspectral measurements: Radiative Transfer assessment ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-17 Masoud Moradi, Behnaz Arabi, Annelies Hommersom, Johan van der Molen, Cyrus Samimi
Automated above-water hyperspectral observations are often subject to inaccuracies caused by instrument malfunction and environmental conditions. This study evaluates the influence of atmospheric and water surface conditions on above-water hyperspectral measurements through statistical methods and Radiative Transfer (RT) modelling. Initially, we developed a general quality control method based on statistical
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EMET: An emergence-based thermal phenological framework for near real-time crop type mapping ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-16 Zijun Yang, Chunyuan Diao, Feng Gao, Bo Li
Near real-time (NRT) crop type mapping plays a crucial role in modeling crop development, managing food supply chains, and supporting sustainable agriculture. The low-latency updates on crop type distribution also help assess the impacts of weather extremes and climate change on agricultural production in a timely fashion, aiding in identification of early risks in food insecurity as well as rapid
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Unifying remote sensing change detection via deep probabilistic change models: From principles, models to applications ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-16 Zhuo Zheng, Yanfei Zhong, Ji Zhao, Ailong Ma, Liangpei Zhang
Change detection in high-resolution Earth observation is a fundamental Earth vision task to understand the subtle temporal dynamics of Earth’s surface, significantly promoted by generic vision technologies in recent years. Vision Transformer is a powerful component to learning spatiotemporal representation but with enormous computation complexity, especially for high-resolution images. Besides, there
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Global Streetscapes — A comprehensive dataset of 10 million street-level images across 688 cities for urban science and analytics ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-16 Yujun Hou, Matias Quintana, Maxim Khomiakov, Winston Yap, Jiani Ouyang, Koichi Ito, Zeyu Wang, Tianhong Zhao, Filip Biljecki
Street view imagery (SVI) is instrumental for sensing urban environments, benefitting numerous domains such as urban morphology, health, greenery, and accessibility. Billions of images worldwide have been made available by commercial services such as Google Street View and crowdsourcing services such as Mapillary and KartaView where anyone from anywhere can upload imagery while moving. However, while
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Explaining the decisions and the functioning of a convolutional spatiotemporal land cover classifier with channel attention and redescription mining ISPRS J. Photogramm. Remote Sens. (IF 10.6) Pub Date : 2024-07-16 Enzo Pelous, Nicolas Méger, Alexandre Benoit, Abdourrahmane Atto, Dino Ienco, Hermann Courteille, Christophe Lin-Kwong-Chon
Convolutional neural networks trained with satellite image time series have demonstrated their potential in land cover classification in recent years. Nevertheless, the rationale leading to their decisions remains obscure by nature. Methods for providing relevant and simplified explanations of their decisions as well as methods for understanding their inner functioning have thus emerged. However, both