Intelligent image segmentation model for remote sensing applications

The conventional JSEG algorithm has a powerful detection capability on the homogeneity of regional texture features because it combines the spectral information with image texture features during the segmentation. However, the conventional JSEG method is not very accurate for the target edge localization in segmentation results. To solve this problem, this paper proposes an improved segmentation method of remotely sensed image based on JSEG algorithm and fuzzy c-means (FCM) with spatial constraints. Firstly, the FCM clustering method based on spatial neighborhood terms is used to replace the traditional HCM clustering method in the quantization step. Then the region growing method is applied to segment the class diagram after FCM clustering. Finally, the proposed method uses the improved regional merger approach to merger the over divided region after segmentation. According to the J index, the proposed algorithm is improved by 31% and 12% compared with the traditional JSEG segmentation method and improved by 17% and 8% compared with the FNEA segmentation algorithm for aerial image and the SPOT 5 image. The experimental results show that the proposed segmentation algorithm has good noise immunity because of the fuzzy clustering of spatial constraints and can extract the edge of the target more accurately.