Texture and pixel-based satellite image classification using cellular automata

Pixel and texture based classification using a well-defined and efficient architecture is considered as a major challenge. Nowadays, a large number of satellite images are received within a fraction of seconds, however processing such images to identify the land cover and land use is considered as a tedious process. To achieve this objective with high accuracy, an algorithm of cellular automata (ACA) is introduced in this proposed approach. The pixel-based classification is carried out with parallelepiped and maximum likelihood classifier, whereas the texture-based classification is accomplished using Softmax regression (SR) classifier. By incorporating ACA, the accuracy of these classification techniques is improved and the performance is then evaluated. This overall classification process is performed to understand the land cover and land use of Kerala. The classification accuracy attained using ACA-based parallelepiped, maximum likelihood and SR is found higher than classical parallelepiped, maximum likelihood, and SR algorithms. The final result reveals that the texture-based ACA classification provides a higher classification accuracy rate (96.8%) than the pixel-based ACA classification (90.98%).