A l1-minimization Based Approach for Hyperspectral Data Classiﬁcation
Most of classiﬁcation methods require model learning procedure or optimal parameters selection using large number of training samples. In this paper, we propose a novel classiﬁcation approach using l1-minimization based sparse representation which does not need any learning procedure or parameters selection. The proposed approach is based on l1minimization because l0-minimization is generally NP-hard and is not a convex optimization problem. Sparse based solutions have been proposed in other areas like signal processing but to the best of our knowledge, this is the ﬁrst time that sparse based classiﬁcation is being proposed for the classiﬁcation of hyperspectral data. l1-minimization based sparse representation is calculated for each of the test samples using few training samples directly. In remote sensing, usually it is extremely difﬁcult and expensive to identify and label the samples due to which we often lack sufﬁcient training samples for classiﬁcation. We tested the proposed approach for difﬁcult classiﬁcation problem i.e high dimensional spaces and few training samples. We also analyzed the cases in which the sparse based classiﬁcation may and may not work. Our extensive experiments on real hyperspectral dataset (AVIRIS 1992 Indiana’s Indian Pines image), prove that the proposed technique offers more classiﬁcation accuracy and more efﬁcient than state-of-the-art SVM and semi-supervised methods as it does not need any model selection.
S. U. H. Qazi et al., "A l1-minimization Based Approach for Hyperspectral Data Classiﬁcation", Key Engineering Materials, Vol. 500, pp. 675-681, 2012