Article
Geochemistry & Geophysics
Xinghua Li, Meizhen He, Huifang Li, Huanfeng Shen
Summary: This study proposes a method based on a multiscale fully convolutional neural network that effectively extracts detailed features of ground objects and addresses the issue of unbalanced samples, with experiments showing better performance in change detection tasks.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Review
Geochemistry & Geophysics
Jianlai Chen, Mengdao Xing, Hanwen Yu, Buge Liang, Jian Peng, Guang-Cai Sun
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2022)
Review
Geography, Physical
F. Nex, C. Armenakis, M. Cramer, D. A. Cucci, M. Gerke, E. Honkavaara, A. Kukko, C. Persello, J. Skaloud
Summary: This paper reviews best practices for the use of UAVs in remote sensing and mapping applications, emphasizes the need for interdisciplinary research, explores the future trends and impacts of UAVs in photogrammetry and remote sensing.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Ayesha Shafique, Guo Cao, Zia Khan, Muhammad Asad, Muhammad Aslam
Summary: This review discusses the importance of change detection in the context of remote sensing technology and the application of deep learning techniques. Deep learning has shown significant success in change detection, outperforming traditional methods, and is considered as the future direction of development.
Article
Geochemistry & Geophysics
Yiding Li, Shunsheng Zhang, Wen-Qin Wang
Summary: A new and faster region-based convolutional neural network (R-CNN) detection method is proposed in this paper, with a new lightweight network design and the use of K-Means method to optimize the recognition of target scale. The proposed method shows significant improvements in both detection performance and speed.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Review
Remote Sensing
Jiaxin Li, Danfeng Hong, Lianru Gao, Jing Yao, Ke Zheng, Bing Zhang, Jocelyn Chanussot
Summary: This survey provides a systematic overview of deep learning-based multimodal remote sensing data fusion. It first introduces essential knowledge in this field, then conducts a literature survey to analyze the trends. It reviews prevalent sub-fields based on different data modalities, and finally collects and summarizes valuable resources and highlights remaining challenges.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Geochemistry & Geophysics
Haifeng Li, Yi Li, Guo Zhang, Ruoyun Liu, Haozhe Huang, Qing Zhu, Chao Tao
Summary: This study proposes a global style and local matching contrastive learning network (GLCNet) for remote sensing image (RSI) semantic segmentation. By using global style contrastive learning and local feature matching contrastive learning modules, the method achieves superior results compared to state-of-the-art methods and supervised learning methods on various RSI semantic segmentation datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Benjamin Dechant, Youngryel Ryu, Grayson Badgley, Philipp Kohler, Uwe Rascher, Mirco Migliavacca, Yongguang Zhang, Giulia Tagliabue, Kaiyu Guan, Micol Rossini, Yves Goulas, Yelu Zeng, Christian Frankenberg, Joseph A. Berry
Summary: Our study highlights the strong relationship between canopy structure and far-red SIF, indicating that the contribution of leaf physiology to SIF variability is small compared to structure and radiation components. The near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) emerges as a robust proxy for far-red SIF across different scales, showing potential for reliable vegetation monitoring at the global level.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Remote Sensing
Liang Lv, Tao Chen, Jie Dou, Antonio Plaza
Summary: Landslide susceptibility mapping is crucial for landslide prevention. This paper proposes a hybrid framework using ensemble learning methods and deep learning models. The proposed model shows improved accuracy and stability compared to single deep learning models, and achieves high performance in testing. Elevation factor is found to be the most influential in landslide susceptibility mapping.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)
Article
Engineering, Electrical & Electronic
Bing Zhang, Yuanfeng Wu, Boya Zhao, Jocelyn Chanussot, Danfeng Hong, Jing Yao, Lianru Gao
Summary: Due to advances in remote sensing satellite imaging and image processing technologies, intelligent remote sensing satellites have the potential to provide personalized, real-time, and accurate remote sensing information services. However, there are technological breakthroughs and legal challenges in the design of intelligent remote sensing satellites.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Swalpa Kumar Roy, Juan M. Haut, Mercedes E. Paoletti, Shiv Ram Dubey, Antonio Plaza
Summary: This article proposes a new 3D-HyperGAMO model to address the issue of imbalanced data in hyperspectral image (HSI) classification. The model uses generative adversarial minority oversampling to automatically generate more samples for minority classes during training, significantly improving the classification performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jianing Wang, Siying Guo, Runhu Huang, Linhao Li, Xiangrong Zhang, Licheng Jiao
Summary: The proposed DcCapsGAN effectively addresses the challenges in hyperspectral image classification and significantly improves the accuracy and performance of classification.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Qingyu Hou, Zhipeng Wang, Fanjiao Tan, Ye Zhao, Haoliang Zheng, Wei Zhang
Summary: This letter proposes a robust infrared small target detection network based on deep learning, which can detect small targets of different sizes and low SNRs in complex backgrounds, and has better effectiveness and robustness against existing algorithms.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Environmental Sciences
Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie, Rabin Chakrabortty, Asish Saha, Thomas Blaschke, Mariano Di Napoli, Omid Ghorbanzadeh, Phuong Thao Thi Ngo
Summary: This paper explores the predictive capacity of different approaches to landslide susceptibility modeling using artificial intelligence. The results show that the CDT-Multiboost model is the excellent model with high accuracy and is effective for improving spatial prediction of landslide susceptibility.
GEOCARTO INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Konrad Heidler, Lichao Mou, Celia Baumhoer, Andreas Dietz, Xiao Xiang Zhu
Summary: This study proposes a new model that combines deep learning methods for segmenting and delineating coastlines. By combining building blocks from different frameworks and using deep supervision and hierarchical attention mechanism, the training effectiveness is improved. The advantages of this approach over traditional methods and other deep learning methods are demonstrated on a challenging dataset of Antarctic coastlines.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Gong Cheng, Xuxiang Sun, Ke Li, Lei Guo, Junwei Han
Summary: The article introduced an effective defense framework PSGAN for RSI scene classification, which trains the classifier by generating examples to combat known and unknown attacks. Experimental results demonstrated the great effectiveness of PSGAN.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Lingjun Li, Xiwen Yao, Gong Cheng, Mingliang Xu, Jungong Han, Junwei Han
Summary: This article aims to achieve one-shot object detection by mimicking human one-shot learning ability, proposing a solo-to-collaborative dual-attention network (SCoDANet) to enhance image feature representations hierarchically. The method effectively solves the issues of extracting related information between query and target images and intractable feature extraction of query class in target images.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Danfeng Hong, Lianru Gao, Renlong Hang, Bing Zhang, Jocelyn Chanussot
Summary: This paper introduces a multimodal deep learning model, EndNet, for hyperspectral and LiDAR data classification, which enhances material identification ability by fusing features and reconstructing multimodal inputs.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Zhiyong Lv, Fengjun Wang, Guoqing Cui, Jon Atli Benediktsson, Tao Lei, Weiwei Sun
Summary: Land cover change detection using remote sensing images is important for various applications. This article proposes a novel neural network method with spatial-spectral attention mechanism and multiscale dilation convolution modules to enhance the detection accuracy. The proposed method achieves superior performance compared to state-of-the-art methods in terms of quantitative evaluation metrics and visual performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Leiyao Liao, Lan Du, Yuchen Guo
Summary: In the field of remote sensing image processing, a method called FDDA is proposed to improve semi-supervised SAR target detection by utilizing a decoding module and a domain-adaptation module. Experimental results show promising performance in SAR target detection with limited labeled SAR images using this approach.
