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June 10, 2026Open Access

A Strategy for Content Based Image Retrieval and Forest Fire Detection From Remotely Sensed Images

Key Points

The aim is to develop effective methods for content based image retrieval and forest fire detection from remotely sensed images.
Implemented CBIR using HSV histograms for image retrieval.
Utilized texture based strategy using Gray level co-occurrence Matrix (GLCM) and Feed Forward Neural Network for detecting forest fires.
Conducted experiments on IRS P6 AWiFS satellite images accessed online.
Successfully retrieved closely matching images using CBIR techniques.
Achieved effective forest fire detection using GLCM and neural networks in the analysis.
Demonstrated the feasibility of automated processing of remotely sensed images for environmental applications.

Abstract

The content based image retrieval (CBIR) of remotely sensed (RS) images is vital in the era of processing huge numbers of remotely sensed images. The paper implements a method for CBIR using HSV histograms for retrieving closely matching images from the database and a texture based strategy for forest fire detection. In texture based strategy Gray level co-occurrence Matrix (GLCM) has been used in combination with Feed Forward Neural Network to detect forest fire. The results presented in this paper were obtained through conducting experiments on IRS P6 AWiFS satellite images downloaded from Internet.

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