Hyperspectral Image Classification Based on Local Binary Patterns and SVDNet

摘要

Hyperspectral image classification is a critical issue in hyperspectral data processing. However, the task has beenacknowledged as extremely challenging due to its characteristics including high dimensionality in data, spatial variabilityof spectral features and scarcity of marked data. In this paper, we propose a new classification method combined withLocal Binary Patterns (LBP) and Singular Value Decomposition Networks (SVDNet). Linear Prediction Error is firstemployed to select informative spectral bands. Then LBP is utilized to extract the texture features. After that, the extractedfeatures of a specified field are transformed to 2-D images. Finally, SVDNet classifies the obtained images and thenthe classification result can be obtained. Experimental results on the real hyperspectral dataset demonstrate that the proposedmethod is capable to achieve higher classification accuracy or at least comparable to existing methods.