Genetic Algorithm Implementation for Improved Change Detection on Remote Sensed Data

Edge detection is about identifying edges in an image. Edges identified in same spot images but captured at different time helps in understanding change detection. Finding the most suitable technique for edge detection is a thought-provoking and time-consuming task. This paper presents an implementation of genetic algorithm on 5 satellite images for edge detection. The proposed technique has been assessed with sobel and canny traditional techniques with the help of entropy values, and it was noted that GA method outperforms the sobel and canny techniques and produces an output image with better clarity and edges for change detection.