改进马尔科夫随机场的SAR图像建筑物分割  

Improved Markov Random Field for Building Segmentation in SAR Images

作　　者：赵佳 安道祥 ZHAO Jia;AN Daoxiang(College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学电子科学学院,湖南长沙410073

出　　处：《遥感技术与应用》2024年第2期405-412,共8页Remote Sensing Technology and Application

基　　金：国家自然科学基金项目(62271492);湖南省杰出青年基金项目(2022JJ10062)。

摘　　要：作为城区主要目标之一,建筑物的检测和提取至关重要,而利用图像分割将建筑物从背景中分离出来是后续处理的基础。传统马尔科夫随机场(Markov Random Field,MRF)模型对合成孔径雷达(Synthetic Aperture Radar,SAR)图像进行建筑物分割时只利用了灰度信息,因此对灰度不均匀目标分割完整性较差,且利用最小能量准则分割时未考虑两部分随机场能量的相互关系,从而导致分割结果不能同时兼顾区域一致性与边缘细节性。为此,研究提出一种改进MRF的SAR图像建筑物分割方法。首先,通过在观测场引入由巴氏距离加权的纹理特征,实现对灰度不均匀建筑物的完整提取;其次,在两部分随机场能量中引入随迭代次数变化的权重,实现在建筑物密集区域保持边缘平滑的同时更好地抑制噪声。为了验证算法的有效性和实用性,对不同场景的SAR图像进行处理,结果表明:所提算法在不同场景中均能得到更好的分类正确率和Dice系数。As one of the main targets in cities,the extraction of buildings is of great importance,and using seg⁃mentation methods to separate buildings from the background is the basis for subsequent information extraction.However,traditional Markov Random Field(MRF)model only uses gray information when segmenting build⁃ings in Synthetic Aperture Radar(SAR)images,so the segmentation integrity of gray inhomogeneous targets is poor,and the interrelationship between the two parts of random field energy is not considered,which leads to the results cannot balance regional consistency and edge detail.In order to solve these problems,an improved MRF model for building segmentation in SAR images is proposed.On the one hand,by introducing texture fea⁃tures weighted by the Bhattacharyya Distance into the observed random field,a complete extraction of gray in⁃homogeneous buildings is achieved;on the other hand,by introducing weights that vary with the number of iter⁃ations in the two parts of random field energy,a better noise suppression is achieved while keeping the edges smooth in the dense region,and finally more accurate building segmentation results are obtained.In order to ver⁃ify the effectiveness and practicability of the proposed algorithm,real SAR images are selected for testing,and the results show that compared with current algorithms,the proposed algorithm has better classification accura⁃cy and Dice coefficient.

关 键 词：马尔科夫随机场 纹理特征 巴氏距离 自适应权重 建筑物分割 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]