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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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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
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High completeness multi-view stereo for dense reconstruction of large-scale urban scenes ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-08 Yongjian Liao, Xuexi Zhang, Nan Huang, Chuanyu Fu, Zijie Huang, Qiku Cao, Zexi Xu, Xiaoming Xiong, Shuting Cai
Multi-View Stereo (MVS) algorithms remain a significant challenge in reconstructing a 3D model with high completeness due to the difficulty in recovering weakly textured regions and detailed parts of large-scale urban scenes. Although the Image Pyramid Structure is a popular approach for dealing with weakly textured regions, it also leads to the loss of detailed information. The proposed method solves
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Trustworthy remote sensing interpretation: Concepts, technologies, and applications ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-08 Sheng Wang, Wei Han, Xiaohui Huang, Xiaohan Zhang, Lizhe Wang, Jun Li
Geographic spaces is a vast and complex system involving multiple elements and nonlinear interactions of these elements, and rich in geographical phenomena, processes and patterns. Artificial intelligence methods (AI) are increasingly utilized to extract information of interest, patterns and insights from massive remote sensing (RS) images. Among them, two representative paradigms for RS interpretation
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Advanced underwater image restoration in complex illumination conditions ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-08 Yifan Song, Mengkun She, Kevin Köser
Underwater image restoration has been a challenging problem for decades since the advent of underwater photography. Most solutions focus on shallow water scenarios, where the scene is uniformly illuminated by the sunlight. However, the vast majority of uncharted underwater terrain is located beyond 200 meters depth where natural light is scarce and artificial illumination is needed. In such cases,
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Deep learning with multi-scale temporal hybrid structure for robust crop mapping ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-07 Pengfei Tang, Jocelyn Chanussot, Shanchuan Guo, Wei Zhang, Lu Qie, Peng Zhang, Hong Fang, Peijun Du
Large-scale crop mapping from dense time-series images is a difficult task and becomes even more challenging with the cloud coverage. Current deep learning models frequently represent time series from a single perspective, which is insufficient to obtain fine-grained details. Meanwhile, the impact of cloud noise on deep learning models is not yet fully understood. In this study, a Multi-scale Temporal
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Three-dimensional lookup table for more precise SAR scatterers positioning in urban scenarios ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-07 Chisheng Wang, Mingxuan Wei, Xiaoqiong Qin, Tao Li, Shuo Chen, Chuanhua Zhu, Peng Liu, Ling Chang
Interferometric Synthetic Aperture Radar (InSAR) applications in urban scenarios require higher geometric accuracy than conventional ones. However, precise positioning of radar scatterers within complex urban structures remains a significant challenge. This study introduces a novel three-dimensional lookup table (3D LUT) positioning method for precise pixel positioning in complex urban environments
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A novel Building Section Skeleton for compact 3D reconstruction from point clouds: A study of high-density urban scenes ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-06 Yijie Wu, Fan Xue, Maosu Li, Sou-Han Chen
Compact building models are demanded by global smart city applications, while high-definition urban 3D data is increasingly accessible by dint of the advanced reality capture technologies. Yet, existing building reconstruction methods encounter crucial bottlenecks against high-definition data of large scales and high-level complexity, particularly in high-density urban scenes. This paper proposes a
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Remote sensing image cloud detection using a shallow convolutional neural network ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-06 Dengfeng Chai, Jingfeng Huang, Minghui Wu, Xiaoping Yang, Ruisheng Wang
The state-of-the-art methods for cloud detection are dominated by deep convolutional neural networks (DCNNs). However, it is very expensive to train DCNNs for cloud detection and the trained DCNNs do not always perform well as expected. This paper proposes a shallow CNN (SCNN) by removing pooling/unpooling layers and normalization layers in DCNNs, retaining only three convolutional layers, and equipping
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3D reconstruction and characterization of cotton bolls in situ based on UAV technology ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-06 Shunfu Xiao, Shuaipeng Fei, Yulu Ye, Demin Xu, Ziwen Xie, Kaiyi Bi, Yan Guo, Baoguo Li, Rui Zhang, Yuntao Ma
Phenotypic traits at the organ scale hold significant importance in the realm of plant breeding, notably in evaluating genetic diversity, selecting innovative cultivars, and forecasting potential yield. Ground platforms, despite delivering accurate results in organ-scale phenotypic studies, frequently exhibit limitations concerning efficiency and adaptability, and can adversely affect the soil. The
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Unveiling China’s natural and planted forest spatial–temporal dynamics from 1990 to 2020 ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-05 Kai Cheng, Haitao Yang, Hongcan Guan, Yu Ren, Yuling Chen, Mengxi Chen, Zekun Yang, Danyang Lin, Weiyan Liu, Jiachen Xu, Guangcai Xu, Keping Ma, Qinghua Guo
Understanding the spatial–temporal dynamics of both natural and planted forests is critical for sustainable forest management and assessing ecological benefits or impacts. In 2020, China’s forest area encompassed approximately 220 million hectares, accounting for around 5 % of the global forest area. Furthermore, China boasts the largest planted forest area worldwide. However, knowledge regarding the
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HD-Net: High-resolution decoupled network for building footprint extraction via deeply supervised body and boundary decomposition ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-05 Yuxuan Li, Danfeng Hong, Chenyu Li, Jing Yao, Jocelyn Chanussot
The extraction of building footprints, as a highly challenging task in remote sensing (RS) image-based geospatial object detection and recognition, holds significant importance. Due to the strong coupling in RS images between the body and boundary of buildings, the ability of most currently advanced deep learning models in building footprint extraction remains limited, inevitably meeting the extraction
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Global mapping of forest clumping index based on GEDI canopy height and complementary data ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-03 Xingmin Zhao, Jing M. Chen, Yongguang Zhang, Ziti Jiao, Liangyun Liu, Feng Qiu, Jinlong Zang, Ruochen Cao
Clumping index (CI), describing the extent of foliage grouping within canopy structures that cause nonrandomness of the leaf spatial distribution within the canopy, is a structural parameter of plant canopies needed in modelling carbon and water cycles of terrestrial ecosystems. CI has been previously retrieved based on an angular index named Normalized Difference between Hotspot and Darkspot (NDHD)
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A new Bayesian semi-supervised active learning framework for large-scale crop mapping using Sentinel-2 imagery ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-03 Yijia Xu, Jing Zhou, Zhou Zhang
Crop mapping provides information on crop types and cropland spatial distribution. Therefore, accurate and timely crop mapping serves as the fundamental step to higher-level agricultural applications, such as crop yield prediction. Recently, deep learning (DL) classification models have been explored for crop mapping. However, most existing methods are still limited in practical applications for large-scale
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Corrigendum to “Validating GEDI tree canopy cover product across forest types using co-registered aerial LiDAR data” [ISPRS J. Photogramm. Remote Sens. 207 (2024) 326–337] ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-02-03 Xiao Li, Linyuan Li, Wenjian Ni, Xihan Mu, Xiaodan Wu, Gaia Vaglio Laurin, Elia Vangi, Krzysztof Stereńczak, Gherardo Chirici, Shiyou Yu, Huaguo Huang
Abstract not available
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A multi-task learning method for extraction of newly constructed areas based on bi-temporal hyperspectral images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-31 Lilin Tu, Xin Huang, Jiayi Li, Jie Yang, Jianya Gong
Newly constructed areas (NCA) are continuously emerging with the development of urbanization, which, however, triggered a series of ecological and environmental issues. Therefore, monitoring NCA is of great significance for sustainable urbanization. Extraction of NCA from remote sensing images involves semantic segmentation of constructed areas and change detection. However, the acquisition of samples
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LiDeNeRF: Neural radiance field reconstruction with depth prior provided by LiDAR point cloud ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-30 Pengcheng Wei, Li Yan, Hong Xie, Dashi Qiu, Changcheng Qiu, Hao Wu, Yinghao Zhao, Xiao Hu, Ming Huang
Neural Radiance Fields (NeRF) is a technique for reconstructing real-world scenes from multiple views. However, existing methods mostly focus on the visual effects of scene reconstruction while neglecting geometric accuracy, which is crucial in photogrammetry and remote sensing. In this paper, we propose a method called LiDeNeRF which uses LiDAR point cloud to provide depth priors for NeRF reconstruction
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Missing information reconstruction integrating isophote constraint and color-structure control for remote sensing data ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-28 Xiaoyu Yu, Jun Pan, Jiangong Xu, Mi Wang
Remote sensing imagery usually suffers from information loss issues due to the sensor defects and harsh atmospheric conditions, resulting in a significant decrease in data quality. In light of this, a novel image reconstruction method is proposed to improve the usability of such data. The proposed method uses the isophote information in reference image as constraint to recover the missing information
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CaR3DMIC: A novel method for evaluating UAV-derived 3D forest models by tree features ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-28 Arvin Fakhri, Hooman Latifi, Kyumars Mohammadi Samani, Fabian Ewald Fassnacht
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Open-set domain adaptation for scene classification using multi-adversarial learning ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-26 Juepeng Zheng, Yibin Wen, Mengxuan Chen, Shuai Yuan, Weijia Li, Yi Zhao, Wenzhao Wu, Lixian Zhang, Runmin Dong, Haohuan Fu
Domain adaptation methods are able to transfer knowledge across different domains, tackling multi-sensor, multi-temporal or cross-regional remote sensing scenarios as they do not rely on labels or annotations in the target domain. However, most of the previous studies have focused on closed-set domain adaptation, based on the assumption that the source and target domains share identical class labels
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Revealing the spatial variation in biomass uptake rates of Brazil’s secondary forests ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-25 Na Chen, Nandin-Erdene Tsendbazar, Daniela Requena Suarez, Celso H.L. Silva-Junior, Jan Verbesselt, Martin Herold
Monitoring forest aboveground biomass (AGB) is essential for quantifying the carbon cycle and mitigating climate change. Tropical secondary forests are significant carbon sinks that sequester large amounts of carbon dioxide. While recent studies have attempted to estimate the AGB recovery rates in tropical forests, considerable uncertainty remains in the estimation of AGB recovery of secondary forests
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Spectral mixture analysis of intimate mixtures for lithological mapping ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-24 Adnan Ahmad, Archana M. Nair
Spectral mixing is frequently encountered in remotely sensed images while imaging heterogeneous surfaces. Spectral mixing occurs at the macroscopic level due to the sensor’s low spatial resolution or multiple reflections from materials received by the sensor. Due to the mixing effect, retrieving the information of spectral endmembers becomes complicated. In this study, two different unmixing methods:
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Remote sensing image classification using an ensemble framework without multiple classifiers ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-22 Peng Dou, Chunlin Huang, Weixiao Han, Jinliang Hou, Ying Zhang, Juan Gu
Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an effective method for improving remote sensing classification accuracy. Although these approaches still follow the conventional pattern of inputting instance features and outputting corresponding classes, they often overlook the intrinsic relationships between pixels beyond their spatial features. As a result, the
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PhenoNet: A two-stage lightweight deep learning framework for real-time wheat phenophase classification ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-18 Ruinan Zhang, Shichao Jin, Yuanhao Zhang, Jingrong Zang, Yu Wang, Qing Li, Zhuangzhuang Sun, Xiao Wang, Qin Zhou, Jian Cai, Shan Xu, Yanjun Su, Jin Wu, Dong Jiang
The real-time monitoring of wheat phenology variations among different varieties and their adaptive responses to environmental conditions is essential for advancing breeding efforts and improving cultivation management. Many remote sensing efforts have been made to relieve the challenges of key phenophase detection. However, existing solutions are not accurate enough to discriminate adjacent phenophases
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Autoencoding tree for city generation and applications ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-19 Wenyu Han, Congcong Wen, Lazarus Chok, Yan Liang Tan, Sheung Lung Chan, Hang Zhao, Chen Feng
City modeling and generation have attracted an increased interest in various applications, including gaming, urban planning, and autonomous driving. Unlike previous works focused on the generation of single objects or indoor scenes, the huge volumes of spatial data in cities pose a challenge to the generative models. Furthermore, few publicly available 3D real-world city datasets also hinder the development
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Assisted learning for land use classification: The important role of semantic correlation between heterogeneous images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-19 Wangbin Li, Kaimin Sun, Wenzhuo Li, Xiao Huang, Jinjiang Wei, Yepei Chen, Wei Cui, Xueyu Chen, Xianwei Lv
In recent times, notable advancements have been achieved in amalgamating heterogeneous remote sensing imagery to facilitate Earth observation through the adoption of convolutional neural networks. Nonetheless, due to the variety in imaging mechanisms and imbalanced information prevalent among heterogeneous data, the efficacious exploitation of semantic correlation across different modalities for generating
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Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-18 Jiabin Chen, Ruohua Hu, Leyan Chen, Zihao Liao, Linlin Che, Tongwen Li
Carbon dioxide (CO2) is one of the most important greenhouse gases in the atmosphere, and carbon satellites play a vital role in monitoring its concentration. However, a single carbon satellite often has inadequate spatial coverage, resulting in numerous gaps. Utilizing the complementary advantages of multiple satellites in spatial coverage for high-coverage mapping of CO2 may be an effective means
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Landsat assessment of variable spectral recovery linked to post-fire forest structure in dry sub-boreal forests ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-18 Sarah M. Smith-Tripp, Nicholas C. Coops, Christopher Mulverhill, Joanne C. White, Jodi Axelson
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ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-13 Sijun Dong, Libo Wang, Bo Du, Xiaoliang Meng
Remote sensing change detection (RSCD), which aims to identify surface changes from bitemporal images, is significant for many applications, such as environmental protection and disaster monitoring. In the last decade, driven by the wave of artificial intelligence, many change detection methods based on deep learning emerged and have achieved essential breakthroughs. However, these methods pay more
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Similarity and dissimilarity relationships based graphs for multimodal change detection ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-13 Yuli Sun, Lin Lei, Zhang Li, Gangyao Kuang
Multimodal change detection (CD) is an increasingly interesting yet highly challenging subject in remote sensing. To facilitate the comparison of multimodal images, some image regression methods transform one image to the domain of the other image, allowing for images comparison in the same domain as in unimodal CD. In this paper, we begin by analyzing the limitations of previous image structure based
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Gaussian meta-feature balanced aggregation for few-shot synthetic aperture radar target detection ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-13 Zheng Zhou, Zongyong Cui, Kailing Tang, Yu Tian, Yiming Pi, Zongjie Cao
Due to the high mobility and strong concealment characteristics of synthetic aperture radar (SAR) targets, the corresponding SAR datasets exhibit few-shot data properties, and there is a significant lack of research on few-shot target detection methods in the SAR domain. Furthermore, this study is subject to the following limitations: the scarcity of SAR data and significant sample variations make
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A continuous digital elevation representation model for DEM super-resolution ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-09 Shun Yao, Yongmei Cheng, Fei Yang, Mikhail G. Mozerov
The surface of the Earth is continuous, and obtaining precise elevation data at arbitrary query positions is essential for many applications and analyses. However, existing digital elevation models suffer from a precision gap caused by discretization. Therefore, we develop a new continuous representation model (CDEM) that allows height values to be obtained at any arbitrary query position. Inspired
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Hierarchical alignment network for domain adaptive object detection in aerial images ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-09 You Ma, Lin Chai, Lizuo Jin, Jun Yan
Domain Adaptive Object Detection (DAOD) alleviates the reliance on labeled data by transferring knowledge learned from labeled source domain to unlabeled target domain. Recent DAOD methods is modeled mainly based on ground-level images. Compared to ground-level images, aerial images suffer from scale variation and viewpoint diversity. This means that domain adaptive object detection in aerial images
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Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-09 Yongjian Sun, Kefeng Deng, Kaijun Ren, Jia Liu, Chongjiu Deng, Yongjun Jin
Nowadays, meteorological data plays a crucial role in various fields such as remote sensing, weather forecasting, climate change, and agriculture. The regional and local studies call for high spatial resolution gridded meteorological data to identify refined details, which however is generally limited due to the models, platforms, sensors, etc. Downscaling has been a significant and practical way to
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Self-supervised pre-training for large-scale crop mapping using Sentinel-2 time series ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-05 Yijia Xu, Yuchi Ma, Zhou Zhang
Large-scale crop mapping is essential for various agricultural applications, such as yield prediction and agricultural resource management. State-of-the-art techniques for crop mapping utilize deep learning (DL) models on satellite imagery time series (SITS). Despite advancements, the efficacy of DL-based crop mapping methods is impeded by the arduous task of acquiring crop-type labels and the extensive
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Validating GEDI tree canopy cover product across forest types using co-registered aerial LiDAR data ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-05 Xiao Li, Linyuan Li, Wenjian Ni, Xihan Mu, Xiaodan Wu, Gaia Vaglio Laurin, Elia Vangi, Krzysztof Stereńczak, Gherardo Chirici, Shiyou Yu, Huaguo Huang
Reliable tree canopy cover (TCC) products are vital for national forest inventory, land process modeling and forest dynamic monitoring. The new generation of space-based laser altimeter, GEDI, offers a three-dimensional (3D) insight on the forest structure, shaping the paradigm of structural variable estimation. However, the generality of newly released GEDI level-2 TCC product version 2 was less investigated
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An efficient urban flood mapping framework towards disaster response driven by weakly supervised semantic segmentation with decoupled training samples ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-05 Yongjun He, Jinfei Wang, Ying Zhang, Chunhua Liao
Despite the proven effectiveness of data-driven deep learning techniques in urban flood mapping, the availability of annotation data remains a critical factor impeding their timeliness in real applications. Recent progress in weakly supervised semantic segmentation (WSSS) presents promising solutions for addressing this limitation. To accomplish prompt and accurate flood mapping in complex urban areas
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Advancing sun glint correction in high-resolution marine UAV RGB imagery for coral reef monitoring ISPRS J. Photogramm. Remote Sens. (IF 12.7) Pub Date : 2024-01-02 Jiangying Qin, Ming Li, Jie Zhao, Deren Li, Hanqi Zhang, Jiageng Zhong
Sun glint presents a significant challenge in marine ecological remote sensing by obscuring valuable features of benthic communities, thus hindering accurate monitoring of these communities. More specifically, sun glint leads to inaccurate coral reef identification when it comes to utilize the high-resolution marine Unmanned Aerial Vehicle (UAV) imagery. Despite the availability of many physical models