The algorithm of building area extraction based on boundary prior and conditional random field for SAR image

In this paper, an algorithm applied for building area extraction on SAR image is proposed, which is based on conditional random model, then a boundary prior relation is introduced to strengthen the description of prior item around the edge of building area, aiming at improving the classification performance nearby the boundary lines encompass building area. Firstly, pre-segmentation and boundary lines extraction can be accomplished respectively rely on mean shift algorithm and ratio of average edge detection. After that a combination term of the distances between the boundary lines and pixels around them and the pixels´ label information can help to improve the prior item in CRF and build the boundary prior-CRF model. Finally, several experimental results on TerraSAR-X images prove that the proposed approach significantly improves the extraction accuracy and classification performance when compared to CRF.