In this paper, we propose a novel hyperspectral image superresolution method based on superpixel spectral unmixing using a coupled encoder-decoder network. The hyperspectral image and multispectral images are fused to generate high-resolution hyperspectral images through the spectral unmixing framework with low-rank constraint. Specifically, the endmember and abundance information is extracted via a coupled encoder-decoder network integrating the priori for unmixing. The coupled network consists of two encoders and one shared decoder, where spectral information is preserved through the encoder. The multispectral image is clustered into superpixels to explore self-similarity, and then, the superpixels are unmixed to obtain an abundance matrix. By imposing a low-rank constraint on the abundance matrix, we further improve the superresolution performance. Experiments on the CAVE and Harvard datasets indicate that our superresolution method outperforms the other compared methods in terms of quantitative evaluation and visual quality.

中文翻译：

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使用耦合的编解码器网络实现超光谱图像超分辨率的超像素光谱分解

在本文中，我们提出了一种新的基于超像素光谱解混的高光谱图像超分辨率方法，该方法采用了耦合的编码器-解码器网络。高光谱图像和多光谱图像通过低阶约束的光谱分解框架融合在一起，生成高分辨率的高光谱图像。具体地，经由整合先验用于解混的耦合的编码器-解码器网络来提取末端成员和丰度信息。耦合网络由两个编码器和一个共享解码器组成，其中频谱信息通过编码器保留。将多光谱图像聚类为超像素以探索自相似性，然后将超像素不混合以获得丰度矩阵。通过在丰度矩阵上施加低秩约束，我们进一步提高了超分辨率性能。在CAVE和Harvard数据集上进行的实验表明，在定量评估和视觉质量方面，我们的超分辨率方法优于其他比较方法。