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

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.