Impact of Feature Extraction and Feature Selection Techniques on Extended Attribute Profile-based Hyperspectral Image Classification

Extended multiattribute profiles (EMAPs) were introduced as morphological profiles built on the features of a hyperspectral image extracted using Principal Component Analysis (PCA). In this paper, we propose to replace PCA with other dimensionality reduction techniques. First, we replace it with Local Fisher Discriminant Analysis (LFDA), a supervised locality preserving DR method. Second, we replace it with two band selection techniques: ICAbs, an Independent Component Analysis (ICA) based band selection, and its modified version that we propose in this article and which we are calling mICAbs. In the experimental part of this paper, we compare the accuracies of classifying the sparse representations of the EMAPs built on features obtained using each of the aforementioned dimensionality reduction techniques. Our experiments reveal that LFDA gives, amongst all, the best classification accuracies. Besides, our proposed modification gives comparable to higher accuracies.