Unsupervised Multilayer Feature Learning for Satellite Image Scene Classification

This letter proposes a simple but effective approach to automatically learn a multilayer image feature for satellite image scene classification. Different from the hand-crafted features which are empirically designed but lack high generalization ability, the proposed approach can autonomously extract the data-dependent feature. The presented feature extraction algorithm is composed of two layers, and the bases of these two layers are uniformly learned by a plain $K$-means clustering algorithm. Coincidentally, the feature extraction performance of the aforementioned two layers is consistent with visual processing of human visual cortex. More specifically, the first layer can generate edgelike bases, which are analogous to the neuron responses of primary visual cortex (V1), and the second layer can produce cornerlike bases, which resemble the neuron responses of visual extrastriate cortical area two (V2). The proposed feature extraction approach can automatically extract not only simple structure features (e.g., edges) but also complex structure features (e.g., corners and junctions). The learned feature is further discriminated by the linear support vector machine classifier for scene classification. In order to fairly demonstrate the validity of the proposed feature extraction approach, its satellite image scene classification performance is evaluated on the public UCM-21 data set. Experimental results show that the proposed approach can outperform several recent state-of-the-art approaches.