Abstract
The traditional local binary pattern (LBP, hereinafter we also call it a two-dimensional local binary pattern 2D-LBP) is unable to depict the spectral characteristics of a hyperspectral image (HSI). To cure this deficiency, this paper develops a joint spectral-spatial 2D-LBP feature (J2D-LBP) by averaging three different 2D-LBP features in a three-dimensional hyperspectral data cube. Subsequently, J2D-LBP is added into the Gabor filter-based deep network (GFDN), and then a novel classification method JL-GFDN is proposed. Different from the original GFDN framework, JL-GFDN further fuses the spectral and spatial features together for HSI classification. Three real data sets are adopted to evaluate the effectiveness of JL-GFDN, and the experimental results verify that (i) JL-GFDN has a better classification accuracy than the original GFDN; (ii) J2D-LBP is more effective in HSI classification in comparison with the traditional 2D-LBP.
Highlights
With the development of technology, more and more tools have been invented for earth observation
This is in agreement with the conclusion in [15], that is, 2.1. Two-Dimensional Local Binary Pattern (2D-LBP)+Gabor filter (GF) is more useful for hyperspectral image (HSI) classification than 2D-LBP or GF
joint spectral-spatial 2D-LBP feature (J2D-LBP) takes into account the spectral characteristics of HSI image, the result of JD-Gabor filter-based deep network (GFDN)* in Figure 11e is better than GFDN*
Summary
With the development of technology, more and more tools have been invented for earth observation. The hyperspectral sensor which can obtain spatially and spectrally continuous data simultaneously has been widely used for various applications, such as medical diagnosis and target detection [1]. Classification using hyperspectral images (HSIs) has attracted a lot of attention in recent years. It is known that HSI often has several hundreds of spectral bands for each pixel, so they can contain much more information than the traditional optical images, and are more beneficial for classification. It is still a challenging task to classify with HSI, since they can produce the Hughes phenomenon [2]. HSI contains highly complex spatial structures and interpixel relations [3]
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