The remote sensed images carry large amount of crucial information. Image processing, a field of signal processing, helps in analysis of remote sensed data. One of the major processing areas is image segmentation with edge detection, which helps in segmenting an image into various sub regions. These regions identified from images, captured over long span of time can help in identification of change detection. This paper presents an application of nature-inspired algorithms viz.: Ant Colony Algorithm, Particle Swarm Optimization and Genetic Algorithm to optimize edge detection procedure. These methods have been implemented on a set of 15 satellite images and further enhancement is done by application of adaptive thresholding using Python. For qualitative analysis, entropy of each output image is computed. The comparison of computer results revealed that particle swarm optimization outperforms conventional methods, i.e., Sobel, Canny and Prewitt as well as ACO and GA. The PSO-based method is able to find more edges and presents far superior quality output images for further analysis with respect to change detection.

遥感影像携带着大量的关键信息。图像处理是信号处理的一个领域，有助于分析遥感数据。主要处理领域之一是使用边缘检测的图像分割，这有助于将图像分割成各个子区域。从图像中识别出的这些区域在很长一段时间内捕获，可以帮助识别变化检测。本文介绍了一种自然启发算法的应用，即：蚁群算法、粒子群优化和遗传算法来优化边缘检测程序。这些方法已在一组 15 个卫星图像上实施，并通过使用 Python 应用自适应阈值进行进一步增强。对于定性分析，计算每个输出图像的熵。计算机结果的比较表明，粒子群优化优于传统方法，即 Sobel、Canny 和 Prewitt，以及 ACO 和 GA。基于 PSO 的方法能够找到更多的边缘，并提供质量高得多的输出图像，以便进一步分析变化检测。