Multi-scale segmentation in change detection for urban high resolution images

In recent years, remote sensing images with high resolution are increasingly applied in change detection and disaster assessment. Compared with the traditional pixel-based methods, object-oriented image processing techniques have attracted more attention for high resolution images. In this paper, we aim to research the object-oriented change detection for urban area. A new multi-scale segmentation algorithm is proposed so as to obtain accurate image objects, and a pre-processing step is adopted to improve the computation efficiency. In order to testify the performance of proposed method, experiments are conducted on QuickBird images. The experimental results show that accurate image objects and changed area can be acquired in appropriate scales.