Article
Computer Science, Artificial Intelligence
Qin Xu, Shiji Liu, Jinpei Liu, Bin Luo
Summary: This paper proposes a cognitively-inspired multi-scale spectral-spatial transformer for HSI super-resolution. By adopting the overlapped band grouping strategy and developing multi-scale spatial and spectral transformer blocks, this method can efficiently learn spatial and spectral features at different scales, as well as long-range dependencies of features. Experimental results demonstrate that the proposed method achieves state-of-the-art results at different scales.
COGNITIVE COMPUTATION
(2023)
Article
Geochemistry & Geophysics
Qin Xu, Shiji Liu, Jiahui Wang, Bo Jiang, Jin Tang
Summary: The article proposes a new method called AS(3)ITransUNet to address the issue of single hyperspectral image super-resolution. The method includes key modules such as spatial-spectral interactive transformer and multistage complementary information learning, which effectively improve spatial resolution and preserve spectral information.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Yameng Wang, Shunping Ji, Yongjun Zhang
Summary: This paper introduces a learnable joint spatial and spectral transformation (JSST) model for remote sensing image retrieval, which adaptsively learns geometric and spectral transformation parameters through a parameter generation network to achieve geometric and spectral correction of images, thus enhancing the generalization and adaptation ability of cross-dataset remote sensing image retrieval.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Xiangtao Zheng, Wenjing Chen, Xiaoqiang Lu
Summary: This study introduces a novel network architecture that can simultaneously explore the spatial and spectral information of multispectral images, leading to the reconstruction of hyperspectral images.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Lei Li, Jihao Yin, Xiuping Jia, Sen Li, Bingnan Han
Summary: The letter proposes a data-driven joint spatial-spectral attention network (JSSAN) to extract more representative features from hyperspectral images by designing spatial-spectral attention blocks and inserting them into a CNN structure. The method outperforms several state-of-the-art algorithms in experimental results.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Geochemistry & Geophysics
Kai Zhang, Anfei Wang, Feng Zhang, Wenxiu Diao, Jiande Sun, Lorenzo Bruzzone
Summary: In this article, a novel pansharpening method based on a spatial and spectral extraction network (SSE-Net) is proposed. Adaptive feature fusion modules (AFFMs) are designed to efficiently merge the features from different subnetworks. The method utilizes spectral ratio loss and gradient loss for effective learning of spatial and spectral features. Experimental results demonstrate that the proposed method outperforms existing techniques in terms of fusion quality.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Xinya Wang, Yingsong Cheng, Xiaoguang Mei, Junjun Jiang, Jiayi Ma
Summary: This paper investigates the challenging problem of hyperspectral image super-resolution (SR) and proposes a novel method named GSSR that improves the reconstruction effect by tweaking the spectral band sequence. Experimental results demonstrate that the proposed method achieves superior performance compared to other methods.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2022)
Article
Geochemistry & Geophysics
Xiaolin Han, Huan Zhang, Jing-Hao Xue, Weidong Sun
Summary: An end-to-end learning strategy using cluster-based multibranch backpropagation neural network is proposed for a spectral and spatial jointed spectral super-resolution method. By incorporating spatial contextual information through modified superpixel segmentation, the method outperforms other state-of-the-art methods on the CAVE dataset. An exemplary application using synchronized observation data collected by multispectral and hyperspectral sensors is also demonstrated.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Bing Yang, Hong Li, Ziyang Guo
Summary: This article proposes a structure-preserving spectral-spatial network (SPSSN) to extract discriminative deep structure-preserving spectral-spatial features by utilizing the manifold structure information during the feature learning process. Experiments on real HSI datasets verify the effectiveness and superiority of the SPSSN compared with several state-of-the-art methods in HSI classification.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Jiaojiao Li, Songcheng Du, Rui Song, Yunsong Li, Qian Du
Summary: This article proposes a novel method called SIGnet, which utilizes a deep convolutional neural network and specific modules to enhance the spectral super-resolution performance of hyperspectral images. Experimental results show that SIGnet outperforms other algorithms in terms of reconstruction performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Shuli Cheng, Liejun Wang, Anyu Du
Summary: In this paper, an ACAS2F2N network is proposed to improve the accuracy of hyperspectral image classification and reduce model complexity through asymmetric coordinate attention spectral-spatial feature fusion. Experimental results show that the proposed algorithm achieves state-of-the-art performance on mainstream datasets.
SCIENTIFIC REPORTS
(2021)
Article
Geochemistry & Geophysics
Kai Yang, Hao Sun, Chunbo Zou, Xiaoqiang Lu
Summary: This paper introduces a cross-attention spectral-spatial network (CASSN) to address the rotation issue in hyperspectral image classification, by extracting spectral and spatial features to determine pixel categories.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Fanqiang Kong, Kedi Hu, Yunsong Li, Dan Li, Xin Liu, Tariq S. Durrani
Summary: In this article, a novel spectral-spatial feature extraction method with polydirectional CNN (SSPC) is proposed for multispectral image compression. The method divides the feature extraction network into spectral and spatial modules, and combines the extracted features through fusion and downsampling. Experimental results show that SSPC outperforms other methods at the same bit rates.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Burkni Palsson, Magnus O. Ulfarsson, Johannes R. Sveinsson
Summary: In this article, a new spectral spatial linear mixture model and an associated estimation method based on a convolutional neural network autoencoder unmixing (CNNAEU) technique were presented. The CNNAEU method successfully exploits both the spatial and spectral structure of hyperspectral images for endmember and abundance map estimation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Ruoxi Song, Yining Feng, Wei Cheng, Zhenhua Mu, Xianghai Wang
Summary: This article proposes a novel bottleneck spatial-spectral transformer (BS2T) for capturing the long-range global dependencies of hyperspectral (HS) image pixels. It replaces convolutional operations with multihead spatial-spectral self-attention (MHS2A) to overcome the limitations of CNN-based HS image classification methods. A dual-branch HS image classification framework based on 3-D CNN and BS2T is defined for extracting local-global features of HS images. Experimental results demonstrate significant improvement compared to state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
