A hybrid multi-scale segmentation approach for remotely sensed imagery

The general image segmentation approach used in other domains may not be applicable to the remote sensing field, which is due to the following factors: remotely sensed data is multi-spectral, always very large in size, and in multi-scale as well. How to quickly and efficiently segment remotely sensed imagery is still a big issue to be solved. Based on human vision mechanism, a new hybrid multi-scale segmentation approach is presented, which is implemented at three coarse-to-fine scale levels. First, remotely sensed imagery is segmented at a coarse scale, and image regions (segments) are produced. Then, the corresponding regions in the original image are segmented by another segmentation approach one by one at the fine scale. From the experiment results, we found the approach is rather promising. However, there still exists some problems to be settled, and further researches should be conducted in the future.