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Geochemistry & Geophysics
Gong Cheng, Liming Cai, Chunbo Lang, Xiwen Yao, Jinyong Chen, Lei Guo, Junwei Han
Summary: SPNet is a lightweight and effective few-shot image classification model, utilizing prototype self-calibration and intercalibration methods to generate more accurate prototypes and predictions through optimizing three loss functions, demonstrating competitive performance compared with other advanced few-shot image classification approaches.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Gaodian Zhou, Weitao Chen, Qianshan Gui, Xianju Li, Lizhe Wang
Summary: The article introduces a method to improve the accuracy of road extraction from high-resolution remote-sensing images using a split depth-wise separable graph convolutional network. The results of the experiment show that this method performs better in extracting covered and tiny roads.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Nico Lang, Nikolai Kalischek, John Armston, Konrad Schindler, Ralph Dubayah, Jan Dirk Wegner
Summary: NASA's GEDI mission aims to advance understanding of forests' role in the global carbon cycle. A novel supervised machine learning approach has been developed to interpret GEDI waveforms and regress canopy top height globally with reliable predictive uncertainty estimates.
REMOTE SENSING OF ENVIRONMENT
(2022)
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Geochemistry & Geophysics
Yi Xiao, Xin Su, Qiangqiang Yuan, Denghong Liu, Huanfeng Shen, Liangpei Zhang
Summary: A novel fusion strategy of temporal grouping projection and an accurate alignment module is proposed for satellite video super-resolution (VSR) in this article. By enhancing the alignment and fusion methods, the spatial resolution and dynamic analysis of satellite videos are effectively improved. Extensive experiments demonstrate that this method outperforms current state-of-the-art VSR methods on Jilin-1 satellite video.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Yibin Ren, Xiaofeng Li, Xiaofeng Yang, Huan Xu
Summary: This study developed a deep learning model, DAU-Net, to classify sea ice and open water from SAR images with high accuracy. By integrating the dual-attention mechanism into U-Net, improvements in pixel-level classification and IoU were achieved. Experimental results showed that DAU-Net outperformed the original U-Net and DenseNetFCN models.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Yule Duan, Hong Huang, Tao Wang
Summary: The article introduces a new method called GSMH, which is a geodesic-based sparse manifold hypergraph, to address the small sample problem in HSI data. This method utilizes geodesic distance and a geodesic-based neighborhood SR model to explore sparse correlations among different manifold neighborhoods, then constructs a pair of semisupervised hypergraphs to obtain nonlinear discriminative feature representation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Gong Cheng, Yanqing Yao, Shengyang Li, Ke Li, Xingxing Xie, Jiabao Wang, Xiwen Yao, Junwei Han
Summary: This article presents a two-stage oriented object detection method, called dual-aligned oriented detector (DODet), to address the spatial and feature misalignment problems. DODet uses an oriented proposal network to generate high-quality proposals and a localization-guided detection head to alleviate the feature misalignment between classification and localization. Extensive evaluations on multiple benchmarks show consistent and substantial improvements compared to baseline methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Rui Li, Shunyi Zheng, Chenxi Duan, Jianlin Su, Ce Zhang
Summary: The study introduces a linear attention mechanism (LAM) to address the issue of increasing memory and computational costs of the dot-product attention mechanism with large-scale inputs, enhancing the flexibility and versatility of integration between attention mechanisms and deep networks.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Review
Geochemistry & Geophysics
Jiangtao Peng, Weiwei Sun, Heng-Chao Li, Wei Li, Xiangchao Meng, Chiru Ge, Qian Du
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2022)
Article
Geochemistry & Geophysics
Chao Tao, Ji Qi, Weipeng Lu, Hao Wang, Haifeng Li
Summary: With the development of deep learning, supervised learning methods have shown good performance in remote sensing image scene classification. However, these methods require a large amount of labeled data for training. In this study, a new self-supervised learning mechanism is introduced to obtain high-performance pretraining models for scene classification from large unlabeled data. Experiments demonstrate that this new paradigm outperforms traditional ImageNet pretrained models, and the insights obtained can contribute to the development of self-supervised learning in the remote sensing community.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Aijin Li, Licheng Jiao, Hao Zhu, Lingling Li, Fang Liu
Summary: This study proposes a semantic boundary awareness network (SBANet) to extract boundary information of land cover in high-resolution remote sensing images. The SBANet utilizes a boundary attention module and adaptive weights of multitask learning to capture and learn boundary information. Experimental results demonstrate the effectiveness of SBANet on 2-D semantic labeling datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Kun Fu, Tengfei Zhang, Yue Zhang, Zhirui Wang, Xian Sun
Summary: The study introduces a metalearning framework named MSAR to tackle the few-shot SAR target classification issue. By analyzing the impact of available training classes on the performance of metalearning models, the few-task problem is defined and addressed using various techniques and methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Remote Sensing
Mohammed A. A. Al-qaness, Ahmed A. Ewees, Hong Fan, Ayman Mutahar AlRassas, Mohamed Abd Elaziz
Summary: This study optimizes the parameters of the ANFIS model using a modified Aquila Optimizer with Opposition-Based learning technique to improve the accuracy of oil production estimation. The proposed AOOBL-ANFIS model outperforms the classic ANFIS model and other compared models, as validated by real-world oil production datasets and various performance metrics.
GEO-SPATIAL INFORMATION SCIENCE
(2022)
Review
Environmental Sciences
Huiwei Jiang, Min Peng, Yuanjun Zhong, Haofeng Xie, Zemin Hao, Jingming Lin, Xiaoli Ma, Xiangyun Hu
Summary: This paper provides a review of the latest progress and challenges in deep learning-based change detection algorithms using high-resolution remote sensing images, and suggests promising directions for future research.
Article
Geochemistry & Geophysics
Peifang Deng, Kejie Xu, Hong Huang
Summary: The study proposed a joint framework, CTNet, combining CNN and ViT to enhance the discriminative ability for HRRS scene classification. The method achieved high classification accuracy on AID and NWPU-RESISC45 datasets, demonstrating superior performance compared to other state-of-the-art methods.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Zhanchao Huang, Wei Li, Xiang-Gen Xia, Hao Wang, Feiran Jie, Ran Tao
Summary: In this article, the authors propose a lightweight oriented object detector (LO-Det) for remote sensing object detection. They design a channel separation-aggregation (CSA) structure and a dynamic receptive field (DRF) mechanism to optimize efficiency and accuracy. They also introduce a diagonal support constraint head (DSC-Head) component to accurately and stably constrain the shape of oriented bounding boxes (OBBs). Experimental results demonstrate that LO-Det achieves fast runtime and competitive accuracy on embedded devices.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Yuli Sun, Lin Lei, Xiao Li, Xiang Tan, Gangyao Kuang
Summary: This article proposes a structure consistency-based method for change detection in remote sensing images. By comparing the structures of two images instead of pixel values, the method demonstrates strong robustness and applicability to various scenarios. Additionally, the method shows effectiveness in both homogeneous and heterogeneous change detection, as well as in the seldom-studied case of heterogeneous change detection with multichannel synthetic aperture radar (SAR) images. Through analysis and improvements on the nonlocal patch-based graph (NLPG), the method is made more accurate and robust.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Libo Wang, Rui Li, Chenxi Duan, Ce Zhang, Xiaoliang Meng, Shenghui Fang
Summary: The fully convolutional network (FCN) with an encoder-decoder architecture is widely used for semantic segmentation. In this paper, the authors propose using the Swin Transformer as the backbone and a novel decoder called DCFAM for better context extraction and resolution restoration. Experimental results on two remotely sensed semantic segmentation datasets demonstrate the effectiveness of the proposed scheme.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Ruitao Lu, Xiaogang Yang, Weipeng Li, Jiwei Fan, Dalei Li, Xin Jing
Summary: A novel small target detection method based on multidirectional derivatives is proposed, which can effectively separate targets from backgrounds. The method constructs a local contrast measure, integrates MDWCM maps from all derivative subbands to enhance detection robustness, and ultimately achieves adaptive segmentation and extraction of small targets.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Wu Xin, Wei Li, Hong Danfeng, Tao Ran, Qian Du
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2022)
