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Feature representation is the key to the hyperspectral images (HSI) inpainting. Existing works mainly focus on using spectral and temporal auxiliary images to inpainting the corrupted region, which were proved to be low robust for all bands missing and high requirements for image acquisition. In this work, we propose an end-to-end inpainting framework for HSI based on convolutional neural networks, which does not require auxiliary images and makes full use of both spectral characteristics and spatial information. For spectral characteristics, a channel attention mechanism is proposed to reduce the redundancy of hyperspectral channels and model the correlation between channels. For spatial information, a local discriminative network is able to cope with the structural continuity of the corrupted regions, and a gradient consistency loss function is proposed to maintain the texture consistency of HSIs. Experimental results in the Airborne Visual Infrared Imaging Spectrometer Indians Pines public dataset and Feicheng Hyperspectral datasets show that our proposed method can provide competitive results compared with state-of-the-art methods.

HSI-IPNet: Hyperspectral Imagery Inpainting by Deep Learning With Adaptive Spectral Extraction