激光与光电子学进展, 2019, 56 (12): 121003, 网络出版: 2019-06-13  

基于密度引力和多尺度多特征融合的遥感影像变化检测 下载: 880次

Remote Sensing Image Change Detection Based on Density Attraction and Multi-Scale and Multi-Feature Fusion
金秋含 1,2,*王阳萍 1,2,**杨景玉 1,2,***
作者单位
1 兰州交通大学电子与信息工程学院, 甘肃 兰州 730070
2 兰州交通大学甘肃省人工智能与图形图像处理工程研究中心, 甘肃 兰州 730070
摘要
针对传统的多特征融合变化检测没有考虑不同特征对变化检测结果的影响及传统的马尔可夫随机场(MRF)变化检测质量受空间信息权重影响的问题,提出了基于密度引力和多尺度多特征融合的变化检测方法。在提取Gabor纹理特征的基础上,通过局部相似性度量和信息熵来获取纹理差异图像,利用变化矢量分析计算了光谱差异图像;采用自适应的方法融合了光谱差异和纹理差异;将密度引力模型与传统MRF相结合,构建了自适应权重的MRF模型,获取了差异图像的变化图。实验结果表明,该方法能够充分利用不同特征,而且能够更好地保持图像边缘细节,提高了变化检测精度。
Abstract
The traditional multi-feature fusion change detection does not consider the fact that different features contribute differently toward the change detection results. Furthermore, the traditional Markov random field (MRF) change detection quality is affected by the spatial information weight. This study proposes a novel change detection method based on density attraction and multi-scale and multi-feature fusion. First, the texture difference image is obtained by local similarity measurement and information entropy on the basis of extracting Gabor texture features, and the spectral difference image is calculated by change vector analysis. Then, the adaptive method is used to fuse the spectral and texture differences. Finally, the density attraction model is combined with the traditional MRF to construct an adaptive weighted MRF model and obtain the change map of a difference image. The experimental results show that the proposed method can not only make full use of different features, but also well maintain the image edge details and improve the change detection accuracy.

金秋含, 王阳萍, 杨景玉. 基于密度引力和多尺度多特征融合的遥感影像变化检测[J]. 激光与光电子学进展, 2019, 56(12): 121003. Qiuhan Jin, Yangping Wang, Jingyu Yang. Remote Sensing Image Change Detection Based on Density Attraction and Multi-Scale and Multi-Feature Fusion[J]. Laser & Optoelectronics Progress, 2019, 56(12): 121003.

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