Learning High-level Features for Satellite Image Classification With Limited Labeled Samples

Abstract

This paper presents a novel method addressing the classification task of satellite images when limited labeled data is available together with a large amount of unlabeled data. Instead of using semi-supervised classifiers, we solve the problem by learning a high-level features, called semisupervised ensemble projection (SSEP). More precisely, we propose to represent an image by projecting it onto an ensemble of weak training (WT) sets sampled from a Gaussian approximation of multiple feature spaces. Given a set of images with limited labeled ones, we first extract preliminary features, e.g., color and textures, to form a low-level image description. We then propose a new semisupervised sampling algorithm to build an ensemble of informative WT sets by exploiting these feature spaces with a Gaussian normal affinity, which ensures both the reliability and diversity of the ensemble. Discriminative functions are subsequently learned from the resulting WT sets, and each image is represented by concatenating its projected values onto such WT sets for final classification. Moreover, we consider that the potential redundant information existed in SSEP and use sparse coding to reduce it. Experiments on high-resolution remote sensing data demonstrate the efficiency of the proposed method.