Rotation invariance through structured sparsity for robust hyperspectral image classification

Sparse representation based classification has gained popularity with geospatial image analysis in general and hyperspectral image analysis in particular. A central idea with such classification approaches is that a test pixel (spectral reflectance vector) can be sparsely represented in a training dictionary of pixels from all classes - in particular, only training pixels in the dictionary that bear the same class membership of the test pixel will contribute significant coefficients in the sparse representation. The traditional applications of such classifiers to hyperspectral imagery utilize pixel (sample) level information, not spatial contextual information. We propose a sparse representation based classification paradigm that effectively and optimally captures the key geometric properties in hyperspectral images - our classifier that is built on this structured sparse representation then offers very robust classification, including in scenarios where training and test objects have rotational variations (a common occurrence with geospatial images). We validate the proposed approach with benchmark hyperspectral data and present results demonstrating the efficacy of the proposed method.