An Object-Based Approach for the Multiscale Image Segmentation of Mountain Forest Scenes

Mountain regions occupy about a quarter of the global terrestrial land surface and provide goods and services to more than half the humanity. However, global environmental change threatens the integrity of mountain ecosystem and their ability to provide the goods and services upon which humanity has come to depend on. In this paper we focused on a semiempirical parameter C is introduced to account for the diffuse radiation and rectify the overcorrection phenomenon. Many studies have adopted this new SCS+C correction and achieved some good results. After that, the mean shift algorithm (MSA) was employed in this study to exploit the remote sensing spatial and spectral information. The application of the mean shift algorithm to remote-sensed images segmentation has been widely reported due to significantly better multiscale segmentation results as other unsupervised classification approaches. Analysis is performed on an Landsat 5 TM image that represents a highly fragmented mountain forested landscape in the Beijing, China.