Segmentation of High-Resolution Remote Sensing Images Using the Gabor Texture Feature-Based Mean Shift Method

High-resolution remote sensing images (HRRSIs) play an important role in the construction and development of society with their rich and detailed information. In the process of remote sensing image segmentation, the conventional method of mean shift usually involves spatial features and spectral features to preserve edge information and reduce the effect of noise. However, this traditional method often misses some important information in HRRSIs processing and reduces the segmentation accuracy. Thus, we proposed an improved mean shift image segmentation method based on Gabor texture features to make full use of the delicate information in HRRSIs. Specifically, the multiscale and multidirectional Gabor filters were employed in this study to extract pixel-to-pixel features from the QuickBird and GeoEye-1 images, and the local variance was used to determine the optimal texture bandwidth. Two parameters, global segmentation (GS) quality index and error rate (ER), were adopted to evaluate the segmentation quality of these two HRRSIs. Results showed that the proposed approach in this study outperforms the traditional mean shift method on both remote sensing datasets. Compared with the traditional method, the improved mean shift approach corresponded to higher GS values and smaller ER values. This study confirms that the improved mean shift approach based on Gabor filtering has a potential for HRRSIs segmentation.