Autoencoder Network for Hyperspectral Unmixing With Adaptive Abundance Smoothing

Abstract

Autoencoder is an efficient technique for unsupervised feature learning, which can be applied to hyperspectral unmixing. In this letter, we present an autoencoder network with adaptive abundance smoothing (AAS) to solve the challenges of previous techniques. Specifically, the proposed method uses a multilayer encoder to obtain the abundance and a single-layer decoder to reconstruct the image. The AAS algorithm tackles the outliers by exploiting the spatial-contextual information and can be adaptive for each pixel. Moreover, the softmax function is used as the encoder output function with the help of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub> regularization to produce sparse output. Experimental results of the synthetic and real data reveal the superior performance of the proposed method against other competitors.