Sparse representation based hyperspectral imagery classification via expanded dictionary
- Resource Type
- Conference
- Authors
- He, Lin; Ruan, Weitong; Li, Yuanqing
- Source
- 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012 4th Workshop on. :1-4 Jun, 2012
- Subject
- Geoscience
Dictionaries
Abstracts
Hyperspectral imaging
Indexes
Programming
Support vector machines
Accuracy
Hyperspectral imagery
classification
sparse representation
dyadic wavelet transform
- Language
- ISSN
- 2158-6268
2158-6276
Recently, pattern classification and recognition based on sparse representation have seen a surge of interest in many applications. In this article, we present a method of sparse representation based hyperspectral imagery classification via expanded dictionary. The original spectral signatures in hyperspectral imagery are transformed with 1-D dyadic wavelet transform. Then these wavelet features are combined with the original spectral signatures to form an expanded dictionary. Finally, linear programming is employed to calculate the sparse solution on such a dictionary which was further substituted into related decision rule. Results of experiment on real hyperspectral imagery validate the effectiveness of our method.