A Method of Interactively Extracting Region Objects from High-Resolution Remote Sensing Image Based on Full Connection CRF

Abstract

Aiming at the region objects of high resolution remote sensing images, this paper proposes an interactive region objects extraction method for high-resolution remote sensing images based on fully connected conditional random fields. This method estimates the foreground model by artificial interaction markers. On the basis of using the SLIC algorithm to over segment the input images, combining the color and texture features, the region-based maximum similarity fusion (MSRM) is used to expand the foreground region and establish the global information of the full-connection conditional random field description image. Then, based on the mean-field estimation, the model inference is realized by the high-dimensional Gauss filtering method, and then the contour of the area features is obtained. The experimental results show that the method is effective by extracting the area features such as waters, woodlands, terraces and bare lands on high resolution remote sensing images.