Enhancement of classification accuracy of multi-spectral satellites? images using Laplacian pyramids

Processed satellites' images are not widely used in remote sensing classification due to the changes in spectral properties that may confuse the classifiers. In pixel-based classification there is a certain debate concerning with the boundary pixels. Most of miss-classified pixels are boundary pixels due to the sudden change in the spectral properties of the contacted objects. This research work is an investigation to study the proper enhancement in classification accuracy that may occur if the Laplacian pyramids are used in classification. The reference map is prepared to study the performance of the proposed system. The Laplacian image is constructed for each band of the satellite image. Then the classification is carried out for both the Laplacian image pyramid and the original satellite image using competitive learning neural networks (CLNN) method. The evaluation is carried out by comparing the classified Laplacian image with the classified original image. A statistical test is carried out to study the significance of using the classified Laplacian image in classification. (c) 2020 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).