Studying the Potentiality of Using Digital Gaussian Pyramids in Multi-spectral Satellites Images Classification

Abstract

Sized satellite images (pyramids) are not widely used in classification due to their dissimilarity with respect to the original image. But varied size images can reduce the cost, the time and mass storage of the classification system and can give us an initial impression of the result. This research work is an investigation to study the dependency upon varied size images in classification instead of using the original satellite image. The reference map is prepared to study the performance of the proposed system. Then, the varied size image is constructed for each band of the satellite image. Then, the classification is carried out using competitive learning neural networks (CLNN) method for all digital image pyramids, either the original satellite image or its sized images. The last step is the evaluation of the studied elements such as accuracy, classification time and storage volume. Some resized images are useful in such application, so the study suggests which size suits such applications.