Volume 12, Issue 7

An Efficient Ontology Based Machine Learning for Classifying High Definition Satellite Images (Chandrayaan-3)

Author

Dr. Chandrakanth Rathod Karahari

Abstract

The analysis of satellite images to determine the kind of land, forest cover, plant type, and other aspects of a particular image has become a standard practice. Object-based image classification for land-cover mapping with remote-sensing data has received a lot of interest in recent years. Several researches have looked into a range of sensors, feature extraction, classifiers, and other important elements throughout the last decade. These data, however, have yet to be gathered into a complete reference on the implications of different guided object-based land-cover categorization methods. To solve this issue, we provide an ontology-based conceptual UKNN classification algorithm in this study. We create an 18-field database from the rit18 database's qualitative and quantitative data in this research. Instead of unexplainable characteristics provided by a CNN, this proposed study uses an ontology-based methodology framework to enable picture classification using features extracted from hyper spectral image data. The image is initially given a Gaussian filter. The suggested UNet K nearest neighbours (UKNN) classifier is utilised to translate the ontology components into image-based parameters, which are then used to improve the classification process. When a typical support vector machine (SVM) is compared to UKNN, we find that UKNN outperforms SVM in classification accuracy

DOI

https://doi.org/10.62226/ijarst20230735

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