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Article
Engineering, Electrical & Electronic
Lei Song et al.
Summary: This work proposes a bibranch fusion network based on axial cross attention (ACABFNet) for change detection research. It effectively combines local and global features extracted by a CNN branch and a transformer branch, respectively, using a bidirectional fusion approach. The experimental results show that ACABFNet outperforms existing change detection algorithms on three datasets.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Kai Hu et al.
Summary: A multi-branch convolutional attention network (MCANet) is proposed for accurate segmentation of cloud/snow regions in remote sensing imagery. The network utilizes a double-branch structure to extract spatial and semantic information, improving feature extraction capability. A fusion module is suggested to correctly merge feature information from multiple branches, and a new decoder module is constructed to enhance information recovery and refine segmentation boundaries.
Article
Green & Sustainable Science & Technology
Zhanming Ma et al.
Summary: This paper proposes a remote sensing image building and water segmentation method based on a local feature search network (DFSNet). By paying attention to the local feature information between horizontal direction and position, it reduces the misjudgment of buildings and loss of local information of water bodies. The introduced discarding attention module (DAM) and slice pooling module (SPM) address the problems of misjudgment and edge blurring, while the fusion attention up sampling module (FAUM) provides better pixel level attention and more detailed segmentation output. Experimental results show a 2.89% improvement on MIoU indicator, with a final MIoU of 83.73%.
Article
Remote Sensing
Shoukuan Miao et al.
Summary: In the application of remote sensing, cloud blocking poses challenges to the analysis of surface and atmospheric parameters. Existing deep learning methods struggle to accurately segment the edge information of clouds and cloud shadows. To address these issues, a multilevel feature enhanced network is proposed for cloud/shadow segmentation. Experimental results demonstrate the superiority of this method over existing approaches.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Chen Lu et al.
Summary: Cloud and cloud shadow detection is a crucial issue in remote sensing image processing. Traditional methods are easily affected by interference and noise, and have rough segmentation results for cloud and cloud shadow boundaries. In order to address these problems, a Multi-scale Strip Pooling Feature Aggregation Network is proposed, which uses a residual network to extract different levels of semantic information. Improved Pyramid Pooling and Mutual Fusion modules are introduced to enhance multi-scale information extraction and fusion capabilities, and a Strip Boundary Refinement module is used to repair the boundary information.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Environmental Sciences
Kai Hu et al.
Summary: This study proposes a multi-scale feature aggregation network for water area segmentation. By designing a deep feature extraction module and a multi-branch aggregation module, it accurately identifies small tributaries in water area images and extracts deep semantic information, achieving improved segmentation accuracy.
Article
Chemistry, Multidisciplinary
Kai Hu et al.
Summary: This paper proposes a novel multi-scale time sampling module and a deep spatiotemporal feature extraction module to enhance the accuracy of human motion recognition network. Comparative experiments show that the proposed method achieves performance improvement on two datasets.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Marine
Kai Hu et al.
Summary: This paper focuses on the importance of underwater video images in human exploration and development of the ocean, analyzes the imaging principle and quality issues of underwater images, and introduces existing enhancement methods and technologies. It also presents standard underwater datasets and evaluation indexes, and discusses possible future developments in this area.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Meteorology & Atmospheric Sciences
Chao Liu et al.
Summary: Cloud Masking is a critical product for satellite remote sensing and downstream applications. This study develops machine learning-based cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite, improving accuracy and reducing misjudgment. Introducing different treatment models for surface conditions and additional surface variables enhances the performance of the ML-based algorithms.
ADVANCES IN ATMOSPHERIC SCIENCES
(2022)
Article
Environmental Sciences
Jiahong Gao et al.
Summary: The use of remote sensing images for land cover analysis has great potential. To improve accuracy, a multichannel feature fusion lozenge network is proposed, which utilizes three branches for feature sampling, contextual information, and feature integration, resulting in significantly improved accuracy.
JOURNAL OF APPLIED REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Zhiwei Wang et al.
Summary: Parameter Identification is important in electric power transmission systems. Existing approaches have limitations in considering development trend of historical data and power grid topology constraints. This work proposes a multi-task graph convolutional neural network (MT-GCN) using GCN and FCN as building blocks, which significantly improves accuracy and robustness in real power transmission systems.
IEEE TRANSACTIONS ON POWER DELIVERY
(2022)
Article
Remote Sensing
Bingyu Chen et al.
Summary: With the improvement of segmentation effect, some studies have been applied to high-resolution remote sensing images. Existing algorithms are limited by short-range context and cannot recover high-resolution details. This paper proposes a multi-level aggregation network (MANet) to optimize the segmentation results by using a global dependency module and a two-path feature refining module.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Environmental Sciences
Enwei Zhang et al.
Summary: In this paper, a multilevel feature context semantic fusion network is proposed to improve the accuracy of cloud and cloud shadow segmentation. The experimental results show that the proposed method is more accurate than the previous network and has good generalization ability on other datasets.
JOURNAL OF APPLIED REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Chen Lu et al.
Summary: This paper proposes a dual-branch model composed of transformer and convolution network for cloud and cloud shadow segmentation in remote sensing images. By introducing a mutual guidance module, the model improves feature extraction and repairs rough segmentation boundaries in the decoding part, demonstrating its effectiveness and superiority compared to existing methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Min Xia et al.
Journal of Applied Remote Sensing
(2021)
Article
Environmental Sciences
Bingyu Chen et al.
Summary: The paper introduces an improved Multi-level Feature Aggregation Network (MFANet) to address the issues of detail loss and fuzzy edges in high-resolution remote sensing image restoration. Experimental results show that the proposed method achieves outstanding performance on the LandCover dataset.
Article
Environmental Sciences
Kai Hu et al.
Summary: Cloud detection is a crucial preprocessing step in optical satellite remote sensing images. Traditional threshold methods and deep-learning methods have limitations, leading to misclassification or loss of spatial information. The proposed CDUNet model utilizes high-frequency feature extractors, multiscale convolutions, spatial prior self-attention, and a dual-attention mechanism to improve cloud detection accuracy and performance on various datasets.
Article
Environmental Sciences
Guangbin Zhang et al.
Summary: In this study, a convolutional neural network CSD-Net was proposed for cloud and snow detection, integrating a multi-scale feature fusion module and a deep supervision mechanism, along with the creation of a high-resolution cloud and snow dataset CSWV. Experimental results show that CSD-Net performs excellently in detection accuracy and efficiency, with fewer parameters and computational operations compared to other state-of-the-art methods.
Article
Computer Science, Interdisciplinary Applications
Yi Qu et al.
Summary: A new method for automatic segmentation of clouds and their shadows in remote sensing images is proposed, using a strip pooling residual network to obtain more accurate local position information and improving segmentation accuracy by combining channel attention and spatial attention.
COMPUTERS & GEOSCIENCES
(2021)
Article
Environmental Sciences
Nan Chen et al.
REMOTE SENSING OF ENVIRONMENT
(2018)
Article
Computer Science, Artificial Intelligence
Vijay Badrinarayanan et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2017)
Article
Geochemistry & Geophysics
Jie Geng et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2015)
Article
Geochemistry & Geophysics
Ming Liu et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2014)
Article
Environmental Sciences
Adrian Fisher
Article
Geochemistry & Geophysics
Corneliu Octavian Dumitru et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2013)
