Imaging Science & Photographic Technology

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Article Geochemistry & Geophysics

Local Similarity-Based Spatial-Spectral Fusion Hyperspectral Image Classification With Deep CNN and Gabor Filtering

Uzair Aslam Bhatti, Zhaoyuan Yu, Jocelyn Chanussot, Zeeshan Zeeshan, Linwang Yuan, Wen Luo, Saqib Ali Nawaz, Mughair Aslam Bhatti, Qurat ul Ain, Anum Mehmood

Summary: This article introduces a novel HSI classification algorithm, LSPGF, which utilizes LSP and Gabor filtering to extract deeper features from original hyperspectral data, achieving higher classification accuracy compared to other algorithms.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Artificial Intelligence for Remote Sensing Data Analysis A Review of Challenges and Opportunities

Lefei Zhang, Liangpei Zhang

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2022)

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Article Geochemistry & Geophysics

Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation

Xin He, Yong Zhou, Jiaqi Zhao, Di Zhang, Rui Yao, Yong Xue

Summary: In this paper, a novel semantic segmentation framework called ST-UNet is proposed for remote sensing images. By incorporating Swin Transformer and CNN, the framework achieves improved segmentation accuracy. The introduced spatial interaction module, feature compression module, and relational aggregation module effectively utilize global context information and local features, leading to significant performance improvements.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Multiscale CNN Based on Component Analysis for SAR ATR

Yi Li, Lan Du, Di Wei

Summary: This article presents a multiscale convolutional neural network (CNN) based on component analysis (CA-MCNN) for synthetic aperture radar (SAR) automatic target recognition (ATR). By combining component information with global information, CA-MCNN achieves a more efficient and robust representation of target features. Extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the superior performance of CA-MCNN.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Computer Science, Artificial Intelligence

Medical image segmentation using deep learning: A survey

Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, Asoke K. Nandi

Summary: This paper is a comprehensive thematic survey on medical image segmentation using deep learning techniques. It classifies and analyzes the literature in a different way, making it more convenient for readers to understand the relevant rationale and guiding them towards improvements in medical image segmentation based on deep learning approaches.

IET IMAGE PROCESSING (2022)

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Article Engineering, Electrical & Electronic

YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning

Wei Liu, Karoll Quijano, Melba M. Crawford

Summary: This paper proposes a tassel detection algorithm based on UAV imagery, which achieves better performance in small-size tassel detection. The algorithm adopts novel techniques such as the bidirectional feature pyramid network and the robust attention module.

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

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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)

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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)

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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)

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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)

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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)

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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)

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Proceedings Paper Computer Science, Artificial Intelligence

Restormer: Efficient Transformer for High-Resolution Image Restoration

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang

Summary: Convolutional neural networks (CNNs) perform well at learning image priors, while Transformers capture long-range pixel interactions. However, the computational complexity of Transformers makes it challenging to apply them to high-resolution image restoration tasks. This work proposes an efficient Transformer model, Restormer, which achieves state-of-the-art results on various image restoration tasks.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022)

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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)

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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)

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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)

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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)

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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)

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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)

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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)

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