An Advanced Semisupervised SVM Classifier for the Analysis of Hyperspectral Remote Sensing Data

摘要

Classification of hyperspectral data is one of the most challenging problems in the analysis of remote sensing images. The complexity of this process depends on both the properties of data (non-stationary spectral signatures of classes, intrinsic high dimensionality) and the practical constraints in ground-truth data collection (which result in a small ratio between the number of training samples and spectral channels). Among the methods proposed in the literature for classification of hyperspectral images, semisupervised procedures (which integrate in the learning phase both labeled and unlabeled samples) and systems based on Support Vector Machines (SVMs) seem to be particularly promising. In this paper we introduce a novel Progressive Semisupervised SVM technique (PS~3VM) designed for the analysis of hyperspectral remote sensing data, which exploits a semisupervised process according to an iterative procedure. The proposed technique improves the one presented in [1,2], exhibiting three main advantages: ⅰ) an adaptive selection of the number of iterations of the semi-supervised learning procedure; ⅱ) an effective model-selection strategy; ⅲ) a high stability of the learning procedure. To assess the effectiveness of the proposed approach, an extensive experimental analysis was carried out on an hyperspectral image acquired by the Hyperion sensor over the Okavango Delta (Botswana).