A Recurrent Feedback Hyperspectral Image Super-Resolution Reconstruction Method by Using Self-Attention-Based Pixel Awareness

IF 4.7 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-10-09 DOI:10.1109/JSTARS.2024.3471899

Ruyi Feng;Zhongyu Guo;Xiaofeng Wang

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引用次数: 0

Abstract

Hyperspectral images (HSIs) contain abundant spectral information, while the spatial resolution is usually limited. To obtain high-spatial-resolution HSIs, various HSI super-resolution (SR) methods are proposed. Currently, deep-learning-based SR reconstruction methods are studied in depth, which take different measures to make full use of the spatial and spectral information of HSIs, and optimize the network with lots of trainings. Although they have achieved satisfying spatial resolution, the spectral consistency before and after reconstruction is difficult to guarantee. In this article, we proposed a self-attention-based recurrent feedback network for hyperspectral SR reconstruction, utilizing pixel-aware weights and pseudo three-dimensional convolution to enhance the spatial and spectral consistency during the reconstruction process. In addition, group reconstruction is used to reduce the redundancy of information. Spectral consistency regularization is proposed to ensure the spectral consistency before and after reconstruction. The effectiveness of the proposed method is tested on one set of natural images and three hyperspectral remote sensing image datasets.

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利用基于自我注意力的像素感知的递归反馈高光谱图像超分辨率重构方法

高光谱图像（HSI）包含丰富的光谱信息，但空间分辨率通常有限。为了获得高空间分辨率的 HSI，人们提出了各种 HSI 超分辨率（SR）方法。目前，基于深度学习的 SR 重建方法得到了深入研究，这些方法采取不同措施充分利用 HSI 的空间和光谱信息，并通过大量训练优化网络。这些方法虽然达到了令人满意的空间分辨率，但重建前后的光谱一致性却难以保证。本文提出了一种用于高光谱 SR 重建的基于自注意的递归反馈网络，利用像素感知权重和伪三维卷积来增强重建过程中的空间和光谱一致性。此外，还利用分组重建来减少冗余信息。还提出了光谱一致性正则化，以确保重建前后的光谱一致性。在一组自然图像和三个高光谱遥感图像数据集上测试了所提方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.