Unsupervised Deep Hyperspectral Inpainting Using a New Mixing Model
Behnood Rasti, Pedram Ghamisi, and Richard Gloaguen
In this paper, we propose a deep learning-based hyperspectral inpainting (DeepHyIn). The proposed approach is unsupervised since it only utilizes the observed image for training the network. First, we propose a novel model for hyperspectral inpainting in which the degraded hyperspectral image is a linear mixture of endmembers and degraded abundances. The proposed model is subjected to abundance sum to one and nonnegativity constraints. We further assume that the endmembers are known. Then, we propose an optimization problem to estimate the unknown abundance using an image prior. Inspired by deep image prior, we shift the optimization problem to optimize the parameters of a deep network. The proposed method uses a deep convolutional encoder-decoder architecture as a backbone. Finally, we apply the DeepHyIn to the Samson dataset and evaluate the results. DeepHyIn demonstrates considerable quantitative and qualitative improvements compared with the state-of-the-art. DeepHyIn was implemented in Python (3.9) using PyTorch as the platform for the deep network and is available on GitHub.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1221-1224, 2022-07-17.