ART Based Reliable Method for Prediction of Agricultural Land Changes Using Remote Sensing  

作　　者：Muthu Pandian Malini Madurai Chidambaram Sashi Kumar N. Sakthieswaran Muthu Pandian Malini;Madurai Chidambaram Sashi Kumar;N. Sakthieswaran(Department of Civil Engineering, Anna University Regional Campus, Tirunelveli, India;Department of Civil Engineering, College of Anna University, Dindugal, India)

机构地区：[1]Department of Civil Engineering, Anna University Regional Campus, Tirunelveli, India [2]Department of Civil Engineering, College of Anna University, Dindugal, India

出　　处：《Circuits and Systems》2016年第6期1051-1067,共17页电路与系统（英文）

摘　　要：This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human errors. To overcome this user friendly GUI based ART2 algorithm has been developed in java to obtain more accuracy in prediction of changes in land. The input is satellite temporal images of the years 1990 and 2014. By using the ART2 algorithm, the input images of the years 1990 and 2014 are classified, where the features are identified to form cluster. The clustered image is given as input and pixel to pixel comparison method in ART2 is implemented in java, for detecting the changes in agricultural lands. The comparison results of ENVI and ARCGIS and GUI based ART2 with in situ data show that the prediction of changes in agricultural land is more accurate in the case of GUI based ART2 implementation.This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human errors. To overcome this user friendly GUI based ART2 algorithm has been developed in java to obtain more accuracy in prediction of changes in land. The input is satellite temporal images of the years 1990 and 2014. By using the ART2 algorithm, the input images of the years 1990 and 2014 are classified, where the features are identified to form cluster. The clustered image is given as input and pixel to pixel comparison method in ART2 is implemented in java, for detecting the changes in agricultural lands. The comparison results of ENVI and ARCGIS and GUI based ART2 with in situ data show that the prediction of changes in agricultural land is more accurate in the case of GUI based ART2 implementation.

关 键 词：ART2 Classification Land Cover Multi Temporal Analysis Land Change Detection Remote Sensing 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]