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A STRATEGY FOR CONTENT BASED IMAGE RETRIEVAL AND FOREST FIRE DETECTION FROM REMOTELY SENSED IMAGES

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. Authors C Jenifer Grace Giftlin, S Jenicka Sethu Institute of Technology, India Keywords Remote Sensing, Histogram, Feature Extraction, Feature Vector, AWiFS

Published By : ICTACT Published In : ICTACT Journal on Soft Computing ( Volume: 10 , Issue: 1 ) Date of Publication : October 2019 DOI :