Application and Result Analysis of Forest Fire Image Retrieval by Weighted Brownian Motion-Based Monarch Butterfly Optimization

Abstract

The Satellite Image Retrieval System uses content to filter out the most pertinent Images from a sea of pictures. The following procedures were used in this work to experiment with satellite photos. In order to improve the contrast of the satellite image while preventing over-enhancement, the AIVA method (Adjusted Intensity based Variant of Adaptive histogram equalization) was used. Using big data and a hybrid feature extraction method, the desired features of satellite images can be accurately retrieved. Effective feature selection in the CBSIR system using the Weighted Brownian Motion-based Monarch Butterfly Optimization (WBMMBO) method with excellent recall, precision, and accuracy. To test the suggested methods in applications for forest fires using the hybrid feature extraction and proposed WBMMBO-based feature selection. By this proposed method the performance can be analyzed and it has been achieved 99% precision, 83% recall, and 90% F-measure.