Kernel eigenmaps based multiscale sparse model for hyperspectral image classification

Hyperspectral imaging (HSI) is the emerging method that combines traditional imaging and spectroscopy to provide the image with both the spatial and spectral information of the object present in the image. The major challenges of the existing techniques for HSI classification are the high dimensionality of data and its complexity in classification. This paper devises a new technique to classify the HSI named Spatial-Spectral Schroedinger Eigen Maps based Mull-scale adaptive sparse representation (S(2)SEMASR). In this, two different phases are employed for the accurate classification of the HSI, namely, Schroedinger Eigen maps (SE) based spatial-spectral feature extraction and mull-scale adaptive sparse classification for the feature extracted image. SE makes use of spatial-spectral cluster potentials which allows the extraction of features that best describes the characteristics of different classes of HSI. The multiscale adaptive sparse representation (MASR) applied over the SE features provides the sparse coefficients that includes distinct scale level sparsity with same class level sparsity. With the obtained coefficients, the class label of each pixel is determined. The proposed HSI classifier well utilizes the spectral and spatial characteristics to exploit the within-class variability and thus reduces the misclassification of similar test pixels Experimental results demonstrated that the proposed S(2)SEMASR approach outperforms the traditional results both qualitatively and quantitatively with an overall accuracy of 98.3%.