Hyperspectral Image Classification via Multiple-Feature-Based Adaptive Sparse Representation

A multiple-feature-based adaptive sparse representation (MFASR) method is proposed for the classification of hyperspectral images (HSIs). The proposed method mainly includes the following steps. First, four different features are separately extracted from the original HSI and they reflect different...

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Bibliographic Details
Published inIEEE transactions on instrumentation and measurement Vol. 66; no. 7; pp. 1646 - 1657
Main Authors Leyuan Fang, Cheng Wang, Shutao Li, Benediktsson, Jon Atli
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptation models
Adaptive algorithms
Adaptive sparse representation
Correlation
Dictionaries
Feature extraction
hyperspectral image classification
Hyperspectral imaging
Image classification
Pixels
Representations
Shape
Sparse matrices
sparse representation
Spatial data
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Summary:A multiple-feature-based adaptive sparse representation (MFASR) method is proposed for the classification of hyperspectral images (HSIs). The proposed method mainly includes the following steps. First, four different features are separately extracted from the original HSI and they reflect different kinds of spectral and spatial information. Second, for each pixel, a shape adaptive (SA) spatial region is extracted. Third, an adaptive sparse representation algorithm is introduced to obtain the sparse coefficients for the multiple-feature matrix set of pixels in each SA region. Finally, these obtained coefficients are jointly used to determine the class label of each test pixel. Experimental results demonstrated that the proposed MFASR method can outperform several well-known classifiers in terms of both qualitative and quantitative results.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2017.2664480