Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Although singular spectrum analysis (SSA) has been successfully applied for data classification in hyperspectral remote sensing, it suffers from extremely high computational cost, especially for 2D-SSA. As a result, a fast implementation of 2D-SSA namely F-2D-SSA is presented in this paper, where the computational complexity has been significantly reduced with a rate up to 60%. From comprehensive experiments undertaken, the effectiveness of F-2D-SSA is validated producing a similar high-level of accuracy in pixel classification using support vector machine (SVM) classifier, yet with a much reduced complexity in comparison to conventional 2D-SSA. Therefore, the introduction and evaluation of F-2D-SSA completes a series of studies focused on SSA, where in this particular research, the reduction in computational complexity leads to potential applications in mobile and embedded devices such as airborne or satellite platforms

Joint kernelized sparse representation classification for hyperspectral imagery

In recent years, the hyperspectral image (HSI) classification has received much attention due to its importance on the military applications, food quality assessment [1], and land cover analysis [2-5], etc. Multiple classifiers have been adopted to label pixels of HSI images, including support vector machine (SVM), random forest (RF), and recently, the deep learning methods. Considering that HSI pixels belonging to the same class are usually lying in a low-dimensional space, those pixels can be represented by training samples from the same class. Based on that, Sparse Representation Classification (SRC) methods have also introduced in the HSI imagery. For an unlabeled pixel, a few atoms from the constructed training dictionary can sparsely represent it. With the recovered sparse coefficients, the class label can be determined by the residual between the test pixel and its approximation. With the development of SRC in HIS [5, 6], there is one severe problem during the process of classification. Due to the high dimensions of the HSI data, it may result the Hughes phenomenon. Sufficient training samples are required to overcome the curse of dimensionality. However, sufficient training data are not always available in real application. For example, the ground truth labelling work for remote sensing data is rather inconvenient. Therefore, to solve the above problem, we decide to combine multiple types of features extracted from HSI data, and a joint kernelized SRC will be operated on those extracted features. The aim of our work is to improve the performance of SRC with less training samples

PCA-Domain Fused Singular Spectral Analysis for Fast and Noise-Robust Spectral-Spatial Feature Mining in Hyperspectral Classification

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral- and spatial-domain feature extraction in hyperspectral images (HSIs). However, PCA itself suffers from low efficacy if no spatial information is combined, while 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this letter a PCA domain 2DSSA approach for spectral-spatial feature mining in HSI. Specifically, PCA and its variation, folded PCA (FPCA) are fused with the 2DSSA, as FPCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational cost can be significantly reduced while preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, our approach can work well on the uncorrected dataset without removing the noisy and water absorption bands, even under a small number of training samples. Experiments on two publicly available datasets have fully validated the superiority of the proposed approach, in comparison to several state-of-the-art methods and deep learning models.</p