王志盼, 沈彦, 王亮, 张清凌, 尤淑撑. 单类分类框架下的高分辨率遥感影像建筑物变化检测算法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(10): 1610-1618. DOI: 10.13203/j.whugis20180485
引用本文: 王志盼, 沈彦, 王亮, 张清凌, 尤淑撑. 单类分类框架下的高分辨率遥感影像建筑物变化检测算法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(10): 1610-1618. DOI: 10.13203/j.whugis20180485
WANG Zhipan, SHEN Yan, WANG Liang, ZHANG Qingling, YOU Shucheng. High-Resolution Remote Sensing Image Building Change Detection Based on One-Class Classifier Framework[J]. Geomatics and Information Science of Wuhan University, 2020, 45(10): 1610-1618. DOI: 10.13203/j.whugis20180485
Citation: WANG Zhipan, SHEN Yan, WANG Liang, ZHANG Qingling, YOU Shucheng. High-Resolution Remote Sensing Image Building Change Detection Based on One-Class Classifier Framework[J]. Geomatics and Information Science of Wuhan University, 2020, 45(10): 1610-1618. DOI: 10.13203/j.whugis20180485

单类分类框架下的高分辨率遥感影像建筑物变化检测算法

High-Resolution Remote Sensing Image Building Change Detection Based on One-Class Classifier Framework

  • 摘要: 针对现有机器学习方法在高分辨率遥感影像建筑物识别等领域需要正负训练样本同时参与, 提出了一种基于一类样本、无需负样本参与的单分类建筑物变化检测算法。首先, 提取影像的形态学建筑物指数特征; 然后与光谱特征进行多特征融合, 并基于该单类分类方法, 从面向对象的角度出发, 得到对象级建筑物变化检测结果; 最后利用构建的一种新的形状特征进行精化, 得到最终的建筑物变化检测结果。通过对多源高分辨率遥感影像开展实验, 验证了该算法具有一定的鲁棒性, 且相比于现有建筑物变化检测算法具有更优的检测精度。

     

    Abstract: In view of the existing machine learning methods in the field of high-resolution remote sensing image building extraction and the like, which requires positive and negative training samples to participate at the same time, a one-class building change detection algorithm based on one-class samples without the need for negative samples is proposed. Firstly, it extracts the morphological building index features of the image, and fuse multi-features with the spectral features. Secondly, based on the one-class classification method proposed in this paper, from the object-based perspective, it gets the object-level building change detection results. Finally, it constructs a new shape feature which is refined to obtain the final building change detection result. Through experiments on multi-source high-resolution remote sensing images, it is verified that our proposed algorithm is robust and has better detection accuracy than existing building change detection algorithms.

     

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