Graphic Characterization of Terrain Using Information Computational Methods

Authors: Saurabh Shandilya

DOI: https://doi.org/10.37082/IJIRMPS.v12.i4.230942

Short DOI: https://doi.org/

Country: India

Full-text Research PDF File:   View   |   Download

Abstract: As is well known, automated pattern recognition and satellite data analysis include a broad spectrum of pattern recognition. Image classification is a crucial remote sensing technology that facilitates the automatic understanding of massive amounts of data. Numerous researchers have put a great deal of effort into this field,and their results are impressive. In order to distinguish between diverse terrain classes, such as water, urban,rocky, vegetation, and barren, we are examining a variety of nature-inspired strategies in this article. the many methods we are using to do this, including the hybrid algorithms of Particle Swarm Optimization (PSO),Artificial Bee Colony (ABC) and Biogeography Based Optimization (BBO), Firefly and Firefly Algorithm, and Hybrid Cuckoo Search & Firefly Algorithm. These methods with a strong natural influence are crucial for applications involving distant sensing. These methods have shown good results showing increased efficiency and larger values of the Kappa coefficient, making them highly helpful for classifying aspects of the terrain. This research examines many methods of classifying satellite photos that draw inspiration from nature.

Keywords: Firefly Based Optimization, Natural Computing, Satellite Image, Remote Sensing, Particle SwarmOptimization, Kappa Coefficient, Image Classification, Cuckoo Search, Artificial bee Colony (ABC) Algorithms

Paper Id: 230942

Published On: 2024-08-06

Published In: Volume 12, Issue 4, July-August 2024

Cite This: Graphic Characterization of Terrain Using Information Computational Methods - Saurabh Shandilya - IJIRMPS Volume 12, Issue 4, July-August 2024. DOI 10.37082/IJIRMPS.v12.i4.230942

Share this