Abstract
This paper investigates representation-based classification for multispectral imagery. Due to small spectral dimension, the performance of classification may be limited, and, in general, it is difficult to discriminate different classes with multispectral imagery. Nonlinear band generation method with explicit functions is proposed to use which can provide additional spectral information for multispectral image classification. Specifically, we propose the simple band ratio function, which can yield better performance than the nonlinear kernel method with implicit mapping function. Two representation-based classifiers—i.e., sparse representation classifier (SRC) and nearest regularized subspace (NRS) method—are evaluated on the nonlinearly generated datasets. Experimental results demonstrate that this dimensionality-expansion approach can outperform the traditional kernel method in terms of high classification accuracy and low computational cost when classifying multispectral imagery.
Highlights
Airborne and spaceborne optical remote sensors collect useful information from the Earth’s surface based on the radiance reflected by different materials
The datasets using nonlinear band generation method are evaluated on sparse representation classifier (SRC), nearest regularized subspace (NRS), their kernel versions with kernel trick (i.e., Kernel sparse representation classifier (KSRC), KNRS), and KSVM
This paper proposes to use nonlinear band generation method with explicit functions for
Summary
Airborne and spaceborne optical remote sensors collect useful information from the Earth’s surface based on the radiance reflected by different materials. The performance of such representation-based classifiers in multispectral image classification is limited, because the low-dimensional pixel vectors cannot offer significant discrepancy in representation residual when using training samples of different classes, producing ambiguity in label assignment. We propose to use a simple strategy to generate artificial bands for multispectral imagery classification The goal of this approach is to use explicit nonlinear functions to contrast the dissimilarity between original spectral measurements, which can provide additional spectral information for classification problems [16]. Our major contribution is to use the simple band ratio as the explicit nonlinear function for dimensionality expansion, which can offer better performance than the traditional kernel method in terms of high classification accuracy and low computational cost.
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