When Pixels Team up Spatially-Weighted Sparse Coding for Hyperspectral Image Classification

TitleWhen Pixels Team up Spatially-Weighted Sparse Coding for Hyperspectral Image Classification
Publication TypeJournal Article
Year of Publication2015
AuthorsSoltani-Farani, A., and H. R. Rabiee
JournalIEEE Geoscience and Remote Sensing Letters
Start Page107
Date Published01/2015
Type of ArticleLetter Paper
Accession Number14525743
Other NumbersINSPEC Accession Number: 14525743
KeywordsClassification, dictionary learning, Hyperspectral Imagery (HSI), linear SVMs., reweighed
AbstractIn this paper, a spatially-weighted sparse unmixing approach is proposed as a front-end for hyperspectral image classification using a linear SVM. The idea is to partition the pixels of a hyperspectral image into a number of disjoint spatial neighborhoods. Since neighboring pixels are often composed of similar materials, their sparse codes are encouraged to have similar sparsity patterns. This is accomplished by means of a reweighted framework where it is assumed that fractional abundances of neighboring pixels are distributed according to a common Laplacian Scale Mixture (LSM) prior with a shared scale parameter. This shared parameter determines which endmembers contribute to the group of pixels. Experiments on the AVIRIS Indian Pines show that the model is very effective in finding discriminative representations for HSI pixels, especially when the training data is limited.
URL<a href="/dmlsite/?q=%3Ca%20href%3D%22/dmlsite/%3Fq%3D%253Ca%2520href%253D%2522/dmlsite/%253Fq%253D%25253Ca%252520href%25253D%252522/dmlsite/%25253Fq%25253D%2525253Ca%25252520href%2525253D%25252522/dmlsite/%2525253Fq%2525253D%252525253Ca%2525252520href%25
DOI<a href="http://dx.doi.org/10.1109/LGRS.2014.2328319&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;am