Remote Sensing

187.4K Papers

Follow See full results

Article Geochemistry & Geophysics

HOG-ShipCLSNet: A Novel Deep Learning Network With HOG Feature Fusion for SAR Ship Classification

Tianwen Zhang, Xiaoling Zhang, Xiao Ke, Chang Liu, Xiaowo Xu, Xu Zhan, Chen Wang, Israr Ahmad, Yue Zhou, Dece Pan, Jianwei Li, Hao Su, Jun Shi, Shunjun Wei

Summary: This article proposes a novel DL network with HOG feature fusion for SAR ship classification. The network has multiple mechanisms to ensure superior classification accuracy. Experimental results demonstrate that HOG-ShipCLSNet significantly outperforms both modern CNN-based methods and traditional hand-crafted feature methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Geography, Physical

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery

Libo Wang, Rui Li, Ce Zhang, Shenghui Fang, Chenxi Duan, Xiaoliang Meng, Peter M. Atkinson

Summary: This paper discusses the importance of semantic segmentation of remotely sensed urban scene images in practical applications, and highlights the advantages of using Transformer and the proposed UNetFormer model for real-time segmentation. Experimental results demonstrate that UNetFormer achieves faster inference speed and higher accuracy compared to state-of-the-art lightweight models.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2022)

添加到收藏夹

Article Geochemistry & Geophysics

A Fast and Compact 3-D CNN for Hyperspectral Image Classification

Muhammad Ahmad, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Mohsin Ali, Muhammad Shahzad Sarfraz

Summary: This study proposes a 3-D CNN model that utilizes both spatial-spectral feature maps to improve the performance of HSIC. By processing small overlapping 3-D patches and generating 3-D feature maps, the model demonstrates remarkable performance in terms of accuracy and computational time.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

添加到收藏夹

Article Environmental Sciences

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Laura Duncanson, James R. Kellner, John Armston, Ralph Dubayah, David M. Minor, Steven Hancock, Sean P. Healey, Paul L. Patterson, Svetlana Saarela, Suzanne Marselis, Carlos E. Silva, Jamis Bruening, Scott J. Goetz, Hao Tang, Michelle Hofton, Bryan Blair, Scott Luthcke, Lola Fatoyinbo, Katharine Abernethy, Alfonso Alonso, Hans-Erik Andersen, Paul Aplin, Timothy R. Baker, Nicolas Barbier, Jean Francois Bastin, Peter Biber, Pascal Boeckx, Jan Bogaert, Luigi Boschetti, Peter Brehm Boucher, Doreen S. Boyd, David F. R. P. Burslem, Sofia Calvo-Rodriguez, Jerome Chave, Robin L. Chazdon, David B. Clark, Deborah A. Clark, Warren B. Cohen, David A. Coomes, Piermaria Corona, K. C. Cushman, Mark E. J. Cutler, James W. Dalling, Michele Dalponte, Jonathan Dash, Sergio de-Miguel, Songqiu Deng, Peter Woods Ellis, Barend Erasmus, Patrick A. Fekety, Alfredo Fernandez-Landa, Antonio Ferraz, Rico Fischer, Adrian G. Fisher, Antonio Garcia-Abril, Terje Gobakken, Jorg M. Hacker, Marco Heurich, Ross A. Hill, Chris Hopkinson, Huabing Huang, Stephen P. Hubbell, Andrew T. Hudak, Andreas Huth, Benedikt Imbach, Kathryn J. Jeffery, Masato Katoh, Elizabeth Kearsley, David Kenfack, Natascha Kljun, Nikolai Knapp, Kamil Kral, Martin Krucek, Nicolas Labriere, Simon L. Lewis, Marcos Longo, Richard M. Lucas, Russell Main, Jose A. Manzanera, Rodolfo Vasquez Martinez, Renaud Mathieu, Herve Memiaghe, Victoria Meyer, Abel Monteagudo Mendoza, Alessandra Monerris, Paul Montesano, Felix Morsdorf, Erik Naesset, Laven Naidoo, Reuben Nilus, Michael O'Brien, David A. Orwig, Konstantinos Papathanassiou, Geoffrey Parker, Christopher Philipson, Oliver L. Phillips, Jan Pisek, John R. Poulsen, Hans Pretzsch, Christoph Rudiger, Sassan Saatchi, Arturo Sanchez-Azofeifa, Nuria Sanchez-Lopez, Robert Scholes, Carlos A. Silva, Marc Simard, Andrew Skidmore, Krzysztof Sterenczak, Mihai Tanase, Chiara Torresan, Ruben Valbuena, Hans Verbeeck, Tomas Vrska, Konrad Wessels, Joanne C. White, Lee J. T. White, Eliakimu Zahabu, Carlo Zgraggen

Summary: This paper presents the development of models used by NASA's Global Ecosystem Dynamics Investigation (GEDI) to estimate forest aboveground biomass density (AGBD). The models were developed using globally distributed field and airborne lidar data, with simulated relative height metrics as predictor variables. The study found that stratification by geographic domain and the use of square root transformation improved model performance.

REMOTE SENSING OF ENVIRONMENT (2022)

添加到收藏夹

Article Geochemistry & Geophysics

Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images

Ruigang Niu, Xian Sun, Yu Tian, Wenhui Diao, Kaiqiang Chen, Kun Fu

Summary: This research proposes a novel attention-based framework named hybrid multiple attention network (HMANet) for semantic segmentation in remote sensing images. The HMANet adaptively captures global correlations by introducing class augmented attention and region shuffle attention modules, improving the efficiency and effectiveness of the self-attention mechanism.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Review Geochemistry & Geophysics

Land Cover Change Detection Techniques: Very-High-Resolution Optical Images: A Review

Lv ZhiYong, Tongfei Liu, Jon Atli Benediktsson, Nicola Falco

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2022)

添加到收藏夹

Article Geochemistry & Geophysics

SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection

Cui Zhang, Liejun Wang, Shuli Cheng, Yongming Li

Summary: This article presents a pure Transformer network called SwinSUNet for remote sensing image change detection. SwinSUNet utilizes the global information extraction ability of Transformers and employs an encoder, fusion module, and decoder to achieve change detection and localization.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Environmental Sciences

Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins

Amirhosein Mosavi, Mohammad Golshan, Saeid Janizadeh, Bahram Choubin, Assefa M. Melesse, Adrienn A. Dineva

Summary: This study proposes novel predictive models for flood and erosion susceptibility mapping in mountainous watersheds. The study also prioritizes the existing sub-basins based on their susceptibility to erosion and flood. A comparative analysis of different models and their ensemble is performed to determine the best model for predicting susceptibility. The results show that the ensemble model has the highest predictive performance and the sub-basins SW3 and SW5 are identified as highly sensitive to flood and soil erosion, respectively.

GEOCARTO INTERNATIONAL (2022)

添加到收藏夹

Article Environmental Sciences

Digital Twin Technology Challenges and Applications: A Comprehensive Review

Diego M. Botin-Sanabria, Adriana-Simona Mihaita, Rodrigo E. Peimbert-Garcia, Mauricio A. Ramirez-Moreno, Ricardo A. Ramirez-Mendoza, Jorge de J. Lozoya-Santos

Summary: A digital twin is a virtual representation of a physical object or process that collects information from the real environment to validate and simulate its present and future behavior. It plays a crucial role in data-driven decision making, complex systems monitoring, product validation and simulation, and object lifecycle management.

REMOTE SENSING (2022)

添加到收藏夹

Review Environmental Sciences

Review of GPM IMERG performance: A global perspective

Rajani K. Pradhan, Yannis Markonis, Mijael Rodrigo Vargas Godoy, Anahi Villalba-Pradas, Konstantinos M. Andreadis, Efthymios I. Nikolopoulos, Simon Michael Papalexiou, Akif Rahim, Francisco J. Tapiador, Martin Hanel

Summary: IMERG products show variation in performance across different geographical locations and climatic conditions, but overall they can appropriately estimate and detect regional precipitation patterns, with room for improvement particularly in mountainous areas and winter precipitation. Despite limitations in reproducing precipitation intensity, each new version of IMERG demonstrates substantial improvement across almost every spatiotemporal scale and climate condition.

REMOTE SENSING OF ENVIRONMENT (2022)

添加到收藏夹

Article Environmental Sciences

Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany

Lukas Blickensdoerfer, Marcel Schwieder, Dirk Pflugmacher, Claas Nendel, Stefan Erasmi, Patrick Hostert

Summary: Monitoring agricultural systems is becoming increasingly important due to global challenges. In this study, we proposed a workflow to generate national agricultural land cover maps on a yearly basis by accounting for varying environmental conditions. Our results showed high overall accuracy and plausible class accuracies for different crop types across years, despite meteorological variability and the presence of drought. The combined use of optical, SAR, and environmental data improved overall accuracies compared to single sensor approaches. The maps demonstrated high spatial consistency and good delineation of field parcels, aligning well with agricultural statistics.

REMOTE SENSING OF ENVIRONMENT (2022)

添加到收藏夹

Article Geochemistry & Geophysics

CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote-Sensing Images

Qi Ming, Lingjuan Miao, Zhiqiang Zhou, Yunpeng Dong

Summary: This article discusses the role of discriminative features in object detection and proposes a critical feature capturing network (CFC-Net) to improve detection accuracy by building powerful feature representation, refining preset anchors, and optimizing label assignment.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Geochemistry & Geophysics

DABNet: Deformable Contextual and Boundary-Weighted Network for Cloud Detection in Remote Sensing Images

Qibin He, Xian Sun, Zhiyuan Yan, Kun Fu

Summary: This article proposes a lightweight network (DABNet) for high-accuracy detection of complex clouds. By introducing a deformable context feature pyramid module and a boundary-weighted loss function, it achieves clearer boundaries and lower false-alarm rate.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Engineering, Electrical & Electronic

Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy, Danfeng Hong, Xin Wu, Jing Yao, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Jocelyn Chanussot

Summary: Hyperspectral imaging (HSI) is widely used in various applications, but the complex characteristics of HSI data make accurate classification challenging for traditional methods. Recent research has shown that deep learning (DL) can effectively address these challenges. This survey provides an overview of DL for HSI classification and compares state-of-the-art strategies. The article also discusses strategies to improve the generalization performance of DL in the context of limited labeled training data.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

添加到收藏夹

Article Geochemistry & Geophysics

Real-Time Processing of Spaceborne SAR Data With Nonlinear Trajectory Based on Variable PRF

Jianlai Chen, Junchao Zhang, Yanghao Jin, Hanwen Yu, Buge Liang, De-Gui Yang

Summary: This article proposes a real-time imaging algorithm based on variable PRF for spaceborne SAR with nonlinear trajectory, which improves imaging accuracy while maintaining high efficiency.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Environmental Sciences

Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees

Rahebeh Abedi, Romulus Costache, Hossein Shafizadeh-Moghadam, Quoc Bao Pham

Summary: This article used different models to analyze the flash-flood susceptibility in the Basca Chiojdului River Basin in Romania, highlighting slope as the most important factor triggering flash floods and identifying the central part of the basin as more susceptible to flash flooding.

GEOCARTO INTERNATIONAL (2022)

添加到收藏夹

Article Environmental Sciences

Fifty years of Landsat science and impacts

Michael A. Wulder, David P. Roy, Volker C. Radeloff, Thomas R. Loveland, Martha C. Anderson, David M. Johnson, Sean Healey, Zhe Zhu, Theodore A. Scambos, Nima Pahlevan, Matthew Hansen, Noel Gorelick, Christopher J. Crawford, Jeffrey G. Masek, Txomin Hermosilla, Joanne C. White, Alan S. Belward, Crystal Schaaf, Curtis E. Woodcock, Justin L. Huntington, Leo Lymburner, Patrick Hostert, Feng Gao, Alexei Lyapustin, Jean-Francois Pekel, Peter Strobl, Bruce D. Cook

Summary: Since 1972, the Landsat program has provided 50 years of digital, multispectral, medium spatial resolution observations, playing a crucial role in scientific and technical advancements. The program's early years brought technological breakthroughs and established a template for global Earth observation missions. The knowledge gained from Landsat has been recognized for its economic and scientific value, leading to continuous improvement and increased usage through the introduction of free and open access to data.

REMOTE SENSING OF ENVIRONMENT (2022)

添加到收藏夹

Article Geochemistry & Geophysics

Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

Zhaokui Li, Ming Liu, Yushi Chen, Yimin Xu, Wei Li, Qian Du

Summary: One of the challenges in hyperspectral image classification is the limited availability of labeled samples. This article introduces a novel deep cross-domain few-shot learning method (DCFSL) to address the domain adaptation and FSL issues in HSI classification. Experimental results demonstrate that DCFSL outperforms existing methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Geochemistry & Geophysics

Information Fusion for Classification of Hyperspectral and LiDAR Data Using IP-CNN

Mengmeng Zhang, Wei Li, Ran Tao, Hengchao Li, Qian Du

Summary: This article proposes an information fusion network called interleaving perception convolutional neural network (IP-CNN) for integrating heterogeneous information and improving joint classification performance of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. The network utilizes a bidirectional autoencoder to reconstruct data and impose perception constraints for multisource structural information integration. Experimental results demonstrate the superiority of the proposed framework.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

添加到收藏夹

Article Environmental Sciences

Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

Xiaowo Xu, Xiaoling Zhang, Tianwen Zhang

Summary: Synthetic aperture radar (SAR) satellites are widely used in maritime monitoring due to their ability to provide microwave remote sensing images without weather and light constraints. However, deploying deep learning-based SAR ship detection methods on satellites is challenging due to their complex models and high computational requirements. In this study, we propose Lite-YOLOv5, a lightweight on-board SAR ship detector based on the You Only Look Once version 5 (YOLOv5) algorithm. Lite-YOLOv5 reduces the model volume, decreases the floating-point operations (FLOPs), and achieves on-board ship detection without sacrificing accuracy.

REMOTE SENSING (2022)

添加到收藏夹

«
1
2
3
4
5
»

See full results

Related Categories

Acoustics (210.4K) Astronomy & Astrophysics (496.7K) Crystallography (223.1K) Energy & Fuels (824.6K) Mineralogy (73.5K) Mining & Mineral Processing (75.2K) Nanoscience & Nanotechnology (726.6K) Nuclear Science & Technology (231.6K) Optics (799K) Physics, Applied (1.7M) Physics, Atomic, Molecular & Chemical (389.4K) Physics, Condensed Matter (779.5K) Physics, Fluids & Plasmas (214.8K) Physics, Mathematical (249.1K) Physics, Multidisciplinary (620.6K) Physics, Nuclear (183.3K) Physics, Particles & Fields (306.2K) Quantum Science & Technology (56K) Spectroscopy (161.7K) Thermodynamics (287.6K)

© Peeref 2019-2024. All rights reserved.