Value based semi automatic segmentation of satellite images using HSV color space, histogram equalization and modified FCM clustering algorithm

Color image segmentation is very useful in many image processing applications. It is possible to identify regions of interest and objects in the scene from the segmentation results, which is very beneficial to the subsequent image analysis. The main objective of the image segmentation is to simplify and change the representation of an image that is easier to analyze. In the satellite image processing, the segmentation is one of the vital step for gathering information from the satellite images. In this paper, an efficient and accurate method of segmentation of satellite images using HSV color space and Modified Fuzzy C Means is proposed. In the HSV color space, the intensity and the color information can easily be separated. Our eye is more sensitive to intensity than color information (hue or saturation). In the proposed approach, the satellite image in RGB color space is transformed into HSV color space and then the transformed satellite image is split into three different components (channels or images) based on intensity and color. The value or intensity component is segmented by modified Fuzzy C Means clustering algorithm after histogram equalization is completed. The proposed approach is applied to analyze the satellite images of various format and size. The experimental result shows that the proposed method is efficient for extracting information from the satellite images.