Satellite Image Segmentation with the Application of PSO Algorithm

Abstract

Satellite imaging consists of capturing images of the Earth through a series of artificial satellites. These images contain an abundance of information that can be used in several applications such as fishing, agriculture, regional planning, biodiversity conservation and many others. Digital image processing can help overcome the limitations of human vision by extracting key information from these images at a much higher rate through the speed of automation. This paper aims to achieve that by exploiting the potential of the Particle Swarm Optimization (PSO) algorithm in image segmentation. Various satellite images were segmented using PSO algorithm before a trace of the objects that have been isolated in the image was run to evaluate the accuracy of segmentation. Three objective measurements which are Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were made on the outputs of the segmentation using PSO algorithm and the traditional segmentation technique which is Otsu’s method for comparison. The proposed method which applies the PSO algorithm proved to be superior in producing images of higher quality and accuracy as compared to the traditional segmentation technique.