Knowledge Based Classifier and Pattern Recognition Technique for Satellite Image Analysis

Abstract

Pattern Recognition is quickly becoming a popular topic of image processing. It is a branch of remote sensing, and it can be useful where it is difficult to visit and analyze geographical locations such as forestry or islands, and it can also be difficult to visit areas affected by natural disasters. To do this, a system to distinguish areas such as buildings, greenery, cultivated land, land, water, and so on must be devised. Previously, research on these themes had been conducted, but it was confined to one or two remote sensor items. This work introduces a method for identifying items such as buildings, greenery, water, and land. Because the knowledge basis for this recognition is based on analysis, it is also unbound to specific types of locations. This method is useful for determining the area under civilization as well as the percentage area of a given pattern. The Image classification technique uses supervised and unsupervised classification methods. The supervised classification uses a maximum likelihood classifier. The unsupervised classification uses the ISO Cluster classifier to classify images. ArcGIS PRO and ERDAS IMAGINE software are used for algorithm analysis.