Author : Ch rajyalch. rajya lakshmi , dr. k.rammohan rao , dr. r.rajeswara rao
Keyword : Satelliteimage,c4.5,lulc,visualinterpretation,regression
Subject : Computer science
Article Type : Original article (research)
DOI : 10.14419/ijet.v7i4.29.21639
Article File : Full Text PDF
Abstract : Andrapradesh is the one of the most important state in India and most of the area covered with coastal line of 974 km (605 mi) with jurisdiction over nearly 15,000 km2 territorial water and this state is the one of the state is the eighth largest state in India covering an area of 162,970 km2(62,920 sq mi). The satellite image data is very large size that means Terabytes of information stored, it includes various patterns and also includes various features like mangrove, water bodies and mining lands, agriculture lands, aquaculture lands were delineated using various classification techniques. In this context, various land use and various features disable threats can be classified and also providing alert services for the emergency management for the future generations using various supervised and unsupervised classification algorithms with various threshold values, we can classifying the data with various visual interpretation techniques and also providing good accuracy and compare the current technique accuracy with previous classification technique accuracy .In the previous classification for satellite data using various supervised classification techniques got accuracies of 78.53% respectively. In the present classification method will proposing the combination of both supervised and unsupervised classification with the good accuracy of 95% with Kmeans and c4.5 with parametric and non parametric regression techniques with Land Use and Land Cover Methods(LULC) with good Data Processing technique with scalable Result
Article by : Ch Rajya lakshmi
Article add date : 2020-12-14
How to cite : Ch rajyalch. rajya lakshmi , dr. k.rammohan rao , dr. r.rajeswara rao. (2020-December-14). Extraction of various features from satellite image data using supervised and unsupervised classification techniques and threat alerts generations for emergency management. retrieved from https://openacessjournal.com/abstract/494