Nonlinear Classification Of Multispectral Imagery Using Representation-Based Classifiers

Keywords

Kernel method; Multispectral imagery; Nonlinear classification

Abstract

The paper investigates representation-based classification for multispectral imagery. Due to the limited spectral dimension, the performance may be limited, and, in general, it is difficult to discriminate different classes using multispectral imagery. Nonlinear band generation method is proposed to use which can provide additional spectral information for multispectral classification. Two classifiers, sparse representation-based classification (SRC) and Nearest Regularized Subspace (NRS) are evaluated on the generated datasets. The results show our approach can outperform other nonlinear method such as the traditional kernel method in terms of classification accuracy and computational cost.

Publication Date

12-1-2017

Publication Title

International Geoscience and Remote Sensing Symposium (IGARSS)

Volume

2017-July

Number of Pages

4738-4741

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IGARSS.2017.8128060

Socpus ID

85041845576 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85041845576

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