Deep spectral unmixing framework via 3D denoising convolutional autoencoder

Hyperspectral unmixing is an important technique which attempts to acquire pure spectra of distinct substances (endmembers) and estimate fractional abundances from highly mixed pixels. This paper proposed a novel deep network‐based framework for unmixing problem. It contains two parts: a three‐dimensional convolutional autoencoder for hyperspectral denoising (denoising 3D CAE) which aims to recover data from highly noised input imagery through an unsupervised manner, and a restrictive non‐negative sparse autoencoder which extracts endmembers and abundances from the scene simultaneously. The proposed denoising 3D CAE network integrates 3D operations in each layer, which allows manipulating volumetric representation of the image data directly and facilitates hierarchical exploration for latent information. Being trained with corrupted hyperspectral image data, the denoising 3D CAE network has strong capacity of capturing the principle and robust local features in spatial and spectral domains efficiently, and it shows superior performance for image recovery with high noise disturbance. Moreover, a part‐based non‐negative autoencoder is concatenated, and the l 2 , 1 ‐norm penalty is imposed for sparsity enhancement of the solution. Comparative experiments are conducted both on synthetic and real‐world hyperspectral data, which demonstrate the applicability and effectiveness of the proposed unmixing framework.