Imitate geometric manifold coverage method for one-class classification of remote sensing data

Abstract

One-class classification (OCC) of remote sensing image only pays attention to the class of interest, without regarding to other classes. Traditional classifiers are inefficient for OCC because it requires all the classes in an image labelled. In this paper, a simple and reliable Imitate Geometric Manifold Coverage (IGMC) method is proposed for solving OCC problems in remote sensing image. First of all, the IGMC method establishes the initial geometric covering space by spectral angle, and the coverage condition using geometry space relationship is presented to determine whether the current testing region is covered in the current geometry. Then, in order to use unlabeled data to help build classifier, the constraint condition using relative correlation dimensions and “Shift and Shrinking” is proposed. Finally, the regions of specific class are labelled as positive label in the whole image. Experiments are conducted using QuickBird multispectral image and MERIS subset. And the results of the proposed framework are compared to Support Vector Data Description, one-class Support Vector Machine (OC-SVM) and object-based SVM methods. The advantages of the new method are that it requires only a small set of training data and provides more accurate and compact classification results.