Semantic discriminant mapping for classification and browsing of remote sensing textures and objects

Abstract

We present a new approach based on discriminant analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1) each dimension corresponds to a semantic likelihood, 2) an efficient and simple multiclass classifier is proposed and 3) it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks.