Fully conv-deconv network for unsupervised spectral-spatial feature extraction of hyperspectral imagery via residual learning

Supervised approaches classify input data using a set of representative samples for each class, known as training samples. The collection of such samples are expensive and time-demanding. Hence, unsupervised feature learning, which has a quick access to arbitrary amount of unlabeled data, is conceptually of high interest. In this paper, we propose a novel network architecture, fully Conv-Deconv network with residual learning, for unsupervised spectral-spatial feature learning of hyperspectral images, which is able to be trained in an end-to-end manner. Specifically, our network is based on the so-called encoder-decoder paradigm, i.e., the input 3D hyperspectral patch is first transformed into a typically lower-dimensional space via a convolutional sub-network (encoder), and then expanded to reproduce the initial data by a deconvolutional sub-network (decoder). Experimental results on the Pavia University hyperspectral data set demonstrate competitive performance obtained by the proposed methodology compared to other studied approaches.