Shapelet-based Sparse Representation for Landcover Classification of Hyperspectral Images
This addresses the problem of improving classification accuracy in hyperspectral imaging for remote sensing applications, but it is incremental as it builds on existing sparse representation techniques.
The paper tackles landcover classification of hyperspectral images by proposing a sparse representation-based approach with a novel dictionary construction that integrates spatial shapelets and spectral information, achieving superior or competitive results compared to existing methods on three datasets.
This paper presents a sparse representation-based classification approach with a novel dictionary construction procedure. By using the constructed dictionary sophisticated prior knowledge about the spatial nature of the image can be integrated. The approach is based on the assumption that each image patch can be factorized into characteristic spatial patterns, also called shapelets, and patch-specific spectral information. A set of shapelets is learned in an unsupervised way and spectral information are embodied by training samples. A combination of shapelets and spectral information are represented in an undercomplete spatial-spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch. The patch-based classification is obtained by means of the representation error. Experiments are conducted on three well-known hyperspectral image datasets. They illustrate that our proposed approach shows superior results in comparison to sparse representation-based classifiers that use only limited spatial information and behaves competitively with or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse representation-based classifiers.