| dc.description.abstract | Many image enhancement algorithms have been developed to improve the appearance of images. However, it is usually difficult to enhance all land cover classes appearing in the satellite images, because local contrast information and details may be lost in the dark and bright areas. In this study, a fuzzy-based image enhancement method is developed to partition the image pixel values into various degrees of associates in order to compensate the local brightness lost in the dark and bright areas. The algorithm contains three stages: First, the satellite image is transformed from gray-level space to membership space by Fuzzy c-Means clustering. Second, appropriate stretch model of each cluster is constructed based on corresponding memberships. Third, the image is transformed back to the gray-level space by merging stretched gray values of each cluster. Various remotely sensed images are used to test the proposed algorithm. There are various land cover classes appearing on the images, including forests, urban areas, river, farm, and so on. Since the gray values of some classes are extremely dark or bright, apparently the global enhancement will result in poor contrast quality. After using the proposed enhancement method, the results are evaluated and compared with other conventional methods. The test results indicate that the proposed method could provide a superior appearance and visualization than conventional enhancement methods. | en_US |