Open AccessConception of the Sflavar Approach: Classification of Satellite Data
Publication year - 2022
Publication title -
international journal of advanced trends in computer science and engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3091
DOI - 10.30534/ijatcse/2022/071112022
Subject(s) - computer science , homogeneous , thematic mapper , variation (astronomy) , artificial intelligence , measure (data warehouse) , satellite , collective intelligence , machine learning , data mining , information retrieval , remote sensing , satellite imagery , geography , mathematics , physics , combinatorics , aerospace engineering , astrophysics , engineering
Unsupervised classification is the search for homogeneous groups in a dataset. This problem is therefore of great complexity and the use of approximation algorithms is inevitable. In this regard, we will call upon a system resulting from a vision of collective intelligence inspired by the behavior of frogs as individuals seeking food, collaborating with each other to accomplish tasks that they cannot perform individually. As a measure of optimization we will make a change by integrating other variation operators. This will be applied to a LANDSAT5 TM (Thematic Mapper) satellite image of the ORAN region.