High Resolution Image Classification Based on Spatio-Temporal Context Model of CRF

Abstract

In addition to the strong correlation in the internal pixels of the image, the pixels in the two phases of image have a certain correlation, that is, temporal context information. Conditional Random Field (CRF) models not only model spatial context information, but also fuse temporal context information. A spatio-temporal model based on temporal context information and spatial context information is proposed to improve the classification accuracy of remote sensing images. Use of two-phase data, the temporal context information between two phases and the spatial context information between pixels within a single temporal phase are considered, and High-order CRF model the spatio-temporal context information. Make full use of spatio-temporal context information of two-phase image to improve the classification accuracy. The experimental results show that the proposed method has better classification accuracy than the classification accuracy without temporal context information.