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Hyperspectral image super-resolution combining with deep learning and spectral unmixing
Signal Processing: Image Communication ( IF 3.5 ) Pub Date : 2020-03-14 , DOI: 10.1016/j.image.2020.115833
Changzhong Zou , Xusheng Huang

In recent years, hyperspectral image super-resolution has attracted the attention of many researchers and has become a hot topic in the field of computer vision. However, it is difficult to obtain high-resolution images due to imaging hardware devices. At present, many existing hyperspectral image super-resolution methods have not achieved good results. In this paper, we propose a hyperspectral image super-resolution method combining with deep residual convolutional neural network (DRCNN) and spectral unmixing. Firstly, the spatial resolution of the image is enhanced by learning a priori knowledge of natural images. The DRCNN reconstructs high spatial resolution hyperspectral images by concatenating multiple residual blocks, each containing two convolutional layers. Secondly, the spectral features of low-resolution and high-resolution hyperspectral images are linked by spectral unmixing. This approach aims to obtain the endmember matrix and the abundance matrix. The final reconstruction result is obtained by multiplying the endmember matrix and the abundance matrix. In addition, in order to improve the visual effect of the reconstructed image, the total variation regularity is used to impose constraints on the abundance matrix to enhance the relationship between the pixels. The experimental results of remote sensing data based on ground facts show that the proposed method has good performance and preserves spatial information and spectral information without the need for auxiliary images.



中文翻译:

高光谱图像超分辨率与深度学习和光谱分解相结合

近年来,高光谱图像的超分辨率吸引了许多研究人员的关注,并已成为计算机视觉领域的热门话题。然而,由于成像硬件设备,难以获得高分辨率图像。目前,许多现有的高光谱图像超分辨率方法尚未取得良好的效果。本文提出了一种结合深残留卷积神经网络(DRCNN)和光谱分解的高光谱图像超分辨率方法。首先,通过学习自然图像的先验知识来增强图像的空间分辨率。DRCNN通过串联多个残差块来重建高分辨率的高光谱图像,每个残差块包含两个卷积层。其次,低分辨率和高分辨率高光谱图像的光谱特征通过光谱分解来链接。该方法旨在获得端基矩阵和丰度矩阵。通过将端成员矩阵和丰度矩阵相乘获得最终的重建结果。另外,为了改善重建图像的视觉效果,使用总变化规律性对丰度矩阵施加约束以增强像素之间的关系。基于地面事实的遥感数据实验结果表明,该方法具有良好的性能,无需辅助图像即可保留空间信息和光谱信息。通过将端成员矩阵和丰度矩阵相乘获得最终的重建结果。另外,为了改善重建图像的视觉效果,使用总变化规律性对丰度矩阵施加约束以增强像素之间的关系。基于地面事实的遥感数据实验结果表明,该方法具有良好的性能,无需辅助图像即可保留空间信息和光谱信息。通过将端成员矩阵和丰度矩阵相乘获得最终的重建结果。另外,为了改善重建图像的视觉效果,使用总变化规律性对丰度矩阵施加约束以增强像素之间的关系。基于地面事实的遥感数据实验结果表明,该方法具有良好的性能,无需辅助图像即可保留空间信息和光谱信息。

更新日期:2020-03-22
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