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Environmental Sciences
Binh Thai Pham, Tran Van Phong, Trung Nguyen-Thoi, Kajori Parial, Sushant K. Singh, Hai-Bang Ly, Kien Trung Nguyen, Lanh Si Ho, Hiep Van Le, Indra Prakash
Summary: In this study, five spatially explicit ensemble predictive machine learning models were developed for landslide susceptibility mapping in the Van Chan district of Vietnam. The results showed that the RSSFT model performed the best in predicting future landslides and was found to be more robust than the other studied models.
GEOCARTO INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Hongjun Su, Zhaoyue Wu, Huihui Zhang, Qian Du
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2022)
Article
Geochemistry & Geophysics
Gong Cheng, Jiabao Wang, Ke Li, Xingxing Xie, Chunbo Lang, Yanqing Yao, Junwei Han
Summary: This article proposes a novel anchor-free oriented proposal generator (AOPG) to address issues in oriented object detection caused by the use of horizontal boxes. The effectiveness of AOPG is demonstrated through extensive experiments, and a new dataset, DIOR-R, is released to alleviate the problem of data insufficiency.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Review
Environmental Sciences
Hojat Shirmard, Ehsan Farahbakhsh, R. Dietmar Muller, Rohitash Chandra
Summary: This paper comprehensively reviews the implementation and adaptation of popular and recently established machine learning methods for processing different types of remote sensing data in mineral exploration. By combining remote sensing data and machine learning methods, the capability to efficiently map critical geological features for potential maps is demonstrated. Moreover, advanced methods such as deep learning show potential to process the new generation of remote sensing data with high spatial and spectral resolution.
REMOTE SENSING OF ENVIRONMENT
(2022)
Review
Geography, Physical
Qiqi Zhu, Xi Guo, Weihuan Deng, Sunan Shi, Qingfeng Guan, Yanfei Zhong, Liangpei Zhang, Deren Li
Summary: A novel semantic change detection framework called Siam-GL was proposed for HSR remote sensing images. The Siam-GL framework effectively extracts representative features of bi-temporal HSR remote sensing images through Siamese architecture and global hierarchical sampling mechanism. Experimental results demonstrated that the Siam-GL framework outperforms advanced semantic change detection methods in terms of both quantity and quality.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jun Yue, Leyuan Fang, Hossein Rahmani, Pedram Ghamisi
Summary: Hyperspectral image (HSI) classification is an important topic in remote sensing, and deep learning-based methods have been widely used. However, the scarcity of labeled samples limits the potential of deep learning-based methods. To address this issue, a self-supervised learning method with adaptive distillation is proposed to train deep neural networks using extensive unlabeled samples.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Yanfeng Liu, Qiang Li, Yuan Yuan, Qian Du, Qi Wang
Summary: In this article, we propose an adaptive balanced network (ABNet) to address the challenges of remote sensing object detection. Our approach utilizes an enhanced effective channel attention mechanism (EECA), an adaptive feature pyramid network (AFPN) and a context enhancement module (CEM) to improve feature representation and capture more discriminative features. Experimental results demonstrate the superior performance of our approach.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Cheng Zhang, Wanshou Jiang, Yuan Zhang, Wei Wang, Qing Zhao, Chenjie Wang
Summary: This article presents a hybrid deep neural network that combines transformer and convolutional neural network (CNN) for semantic segmentation of very high resolution remote sensing imagery. The network utilizes a new universal backbone Swin transformer for feature extraction and incorporates various strategies for multiscale context modeling. It achieves improved accuracy through skip connections and an auxiliary boundary detection branch.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Yao Ding, Xiaofeng Zhao, Zhili Zhang, Wei Cai, Nengjun Yang
Summary: SAGE-A uses a multi-level graph sample and aggregate (graphSAGE) network to flexibly aggregate new neighbor nodes among arbitrarily structured non-Euclidean data and capture long-range contextual relations. The network utilizes the graph attention mechanism to characterize the importance among spatially neighboring regions, allowing for automatic learning of deep contextual and global information of the graph.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Liang Zhou, Yuanxin Ye, Tengfeng Tang, Ke Nan, Yao Qin
Summary: This study employs deep learning techniques to enhance image structure features for improved matching between SAR and optical images, showing advantages over other methods in experimental results.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Lianru Gao, Zhu Han, Danfeng Hong, Bing Zhang, Jocelyn Chanussot
Summary: In recent years, deep learning has gained attention in hyperspectral unmixing applications. However, traditional autoencoder frameworks tend to lose important detailed information. This study proposes a cycle-consistency unmixing network (CyCU-Net) to enhance the unmixing performance by learning two cascaded autoencoders and introducing a self-perception loss to achieve cycle consistency. Experimental results demonstrate that CyCU-Net outperforms other algorithms in terms of effectiveness and competitiveness.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Chunyan Yu, Rui Han, Meiping Song, Caiyu Liu, Chein-I Chang
Summary: This article presents a spatial-spectral dense CNN framework called FADCNN for hyperspectral image classification, addressing the problems of high complexity, information redundancy, and inefficient description in current networks. Experimental results show that the proposed FADCNN architecture has significant advantages compared with other state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Qibin He, Xian Sun, Zhiyuan Yan, Beibei Li, Kun Fu
Summary: This paper proposes an end-to-end online framework called TGraM for multi-object tracking in satellite videos. It also builds a large-scale satellite video dataset for experiments. Compared with existing multi-object trackers, TGraM achieves efficient collaborative learning between detection and reidentification, improving tracking accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Xiangtao Zheng, Binqiang Wang, Xingqian Du, Xiaoqiang Lu
Summary: This study introduces a method for remote sensing visual question answering (VQA) that considers the fusion of image features and question features, introducing convolutional features and word vectors, as well as attention mechanism and bilinear technique. Experimental results demonstrate that the proposed method can capture the alignments between images and questions.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Zilong Zhong, Ying Li, Lingfei Ma, Jonathan Li, Wei-Shi Zheng
Summary: This study introduces a novel spectral-spatial transformer network (SSTN) to overcome the limitations of convolution kernels and proposes a factorized architecture search (FAS) framework that focuses on finding optimal architecture settings without the need for bilevel optimization. Experimental results demonstrate the excellent performance of SSTNs on multiple HSI benchmark datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Runmin Cong, Yumo Zhang, Leyuan Fang, Jun Li, Yao Zhao, Sam Kwong
Summary: This article proposes a relational reasoning network (RRNet) with parallel multiscale attention (PMA) for salient object detection (SOD) in optical remote sensing images (RSIs). By integrating the spatial and channel dimensions and utilizing high-level encoder features, the RRNet infers semantic relationships and generates more complete detection results. The PMA module effectively restores detailed information and addresses scale variation of salient objects.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Xuan Kong, Chunping Yang, Siying Cao, Chaohai Li, Zhenming Peng
Summary: This article proposes a new infrared patch-tensor (IPT) model to address the challenges of inaccurate background rank representation and poor robustness against noise and sparse interference. Experimental results demonstrate the robustness of the algorithm to noise and different scenes, and show significant superiority in detection performance compared with other methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Kunping Yang, Gui-Song Xia, Zicheng Liu, Bo Du, Wen Yang, Marcello Pelillo, Liangpei Zhang
Summary: The article presents an asymmetric Siamese network (ASN) for locating and identifying semantic changes through feature pairs from modules of widely different structures to deal with distinct land-cover distributions at different times. The experimental results show that the proposed model can consistently outperform existing algorithms with varying encoder backbones.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jing Bai, Bixiu Ding, Zhu Xiao, Licheng Jiao, Hongyang Chen, Amelia C. Regan
Summary: This paper proposes a framework based on a deep attention graph convolutional network (DAGCN) to address the challenges of hyperspectral image classification. By integrating attention mechanism and designing deep graph convolutional networks, deep abstract features are extracted and the internal relationship between HSI data is explored, achieving superior classification results compared to the baselines.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Hao Chen, Wenyuan Li, Zhenwei Shi
Summary: The article introduces a novel data augmentation method IAug to generate bitemporal images with diverse building changes using generative adversarial training. A simple yet effective CD model CDNet is proposed, achieving state-of-the-art results in building change detection.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
