A Kernel Method Based on Topic Model for Very High Spatial Resolution (Vhsr) Remote Sensing Image Classification

Abstract

ABSTRACT:A kernel-based method for very high spatial resolution remote sensing image classification is proposed in this article. The new kernel method is based on spectral-spatial information and structure information as well, which is acquired from topic model, Latent Dirichlet Allocation model. The final kernel function is defined as ,function (RBF) and 1 2 3 1 u u u    . In the experiment, comparison with three other kernel methods, including the spectral-based, the spectral-and spatial-based and the spectral-and structure-based method, is provided for a panchromatic QuickBird image of a suburban area with a size of 900×900 pixels and spatial resolution of 0.6m. The result shows that the overall accuracy of the spectral-and structure-based kernel method is 80%, which is higher than the spectral-based kernel method, as well as the spectral-and spatial-based which accuracy respectively is 67% and 74%. What's more, the accuracy of the proposed composite kernel method that jointly uses the spectral, spatial, and structure information is highest among the four methods which is increased to 83%. On the other hand, the result of the experiment also verifies the validity of the expression of structure information about the remote sensing image.