A Region Segmentation Algorithm for Remote Sensing Imaging Combined with Multi-feature and Multi-band

High spatial resolution remote sensing images provide many rich features, such as spectrum, shape, texture, etc. However, only spectral character is adopted in many traditional image segmentation methods, leading to segmentation results unsatisfactory. A multi-feature and multi-band region segmentation algorithm (MM-RSA) is proposed. First, texture image of a band is extracted and is combined into multi-spectral image. Second, seed region is selected from the combined multi-spectral image using Fuzzy C-Means Clustering method. Third, the segmentation process is performed by employing a region growing criterion, which integrates spectral and shape feature information. The algorithm not only integrates the criterions of spectrum, texture and shape, but also is of multi-scale characteristic. Experiments were conducted on a QuickBird image to evaluate the performance, and the results showed that the MM-RSA is able to effectively obtain segmentation results at different scales, and the overall performance of segmentation is improved when compared with pixel-based segmentation algorithm and multi-resolution-based segmentation algorithm.