Abstract
Spatial texture features have been demonstrated to be very useful for the recently-proposed representation-based classifiers, such as the sparse representation-based classifier (SRC) and nearest regularized subspace (NRS). In this work, a weighted residual-fusion-based strategy with multiple features is proposed for these classifiers. Multiple features include local binary patterns (LBP), Gabor features, and the original spectral signatures. In the proposed classification framework, representation residuals for a testing pixel from using each type of features are weighted to generate the final representation residual, and then the label of the testing pixel is determined according to the class yielding the minimum final residual. The motivation of this work is that different features represent pixels from different perspectives and their fusion in the residual domain can enhance the discriminative ability. Experimental results of several real hyperspectral image datasets demonstrate that the proposed residual-based fusion outperforms the original NRS, SRC, support vector machine (SVM) with LBP, and SVM with Gabor features, even in small-sample-size (SSS) situations.
Highlights
Containing hundreds of spectral narrow bands, hyperspectral imagery has a very high spectral resolution that provides potential for more accurate object classification
It is apparent that the proposed RF-nearest regularized subspace (NRS) and RF-sparse representation-based classifier (SRC) still provide superior performance, which further affirms that classification accuracy can be greatly improved by fusing two complementary spatial features (i.e., Gabor features and local binary patterns (LBP) features)
It was found that the resulting classifiers, i.e., RF-NRS and RF-SRC, were more discriminative than the original spectral classifiers and the classifiers with the Gabor feature and the LBP feature only
Summary
Containing hundreds of spectral narrow bands, hyperspectral imagery has a very high spectral resolution that provides potential for more accurate object classification. Nearest regularized subspace (NRS) [3] was proposed for hyperspectral image classification, where each testing pixel was represented by a linear combination of all available labeled samples per class and the class label was the one whose training samples provide the lowest representation residual. Along with algorithm development for hyperspectral image classification, feature-level and decision-level fusion have been investigated [23,30,31]. In the proposed classification framework, each type of feature is used for the NRS or SRC, generating multiple representation residuals, and all these residuals are added together with different weights [37] and the label of the testing pixel is determined according to the class yielding the minimum weighted sum of the residuals. It is expected that different types of spatial features reflect the characteristics of a pixel from different perspectives and their fusion in the residual domain is able to enhance class separability even in small-sample-size (SSS) situations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
