SEMI-SUPERVISED FEATURE LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION

This paper presents a semi-supervised method for learning informative image representations, which is a crucial but challenging step for remote sensing image classification. More precisely, we propose to represent an image by projecting it onto an ensemble of prototype sets sampled from a Gaussian approximation of multiple feature spaces. Given a set of images with a few labeled ones, we first extract preliminary features, e.g. color and textures, to form a low-level image description. We then build an ensemble of informative prototype sets by exploiting these feature spaces with a Gaussian normal affinity. Discriminative functions are subsequently learned from the resulting prototype sets, and each image is represented by concatenating their projected values onto such prototypes for final classification. Experiments on two high-resolution remote sensing image sets demonstrate the efficiency of the proposed method on remote sensing image classification with different classifiers.