Title :
Automatic land use/land cover classification using texture and data mining classifier
Author :
Bharathi, S. ; Manju, M. ; Vasavi Manasa, C.L. ; Mallika, H.M. ; Maruti, M. Kurule ; Deepa, Shenoy P. ; Venugopal, K.R. ; Patnaik, L.M.
Author_Institution :
Dept. of MCA, Bangalore Univ., Bangalore, India
Abstract :
Nowadays everywhere remote sensing images are used for wide variety of applications, creation of mapping products for military and civil applications, evaluation of environmental damage, monitoring of land use, radiation monitoring, urban planning, growth regulation, soil assessment, and crop yield appraisal. A few number of image classification algorithms have proved good precision in classifying remote sensing data. An efficient classifier is needed to classify the remote sensing imageries to extract information. We have used texture based supervised classification. Here we compared different classification methods. KNN, SVM and Neural network are used. All the three classifier gives good result but neural network classifier takes long time, the time complexity is very high. Land use mapping has been done by comparing the images and area of the land used is calculated.
Keywords :
geophysical image processing; image classification; image texture; learning (artificial intelligence); neural nets; support vector machines; terrain mapping; KNN; SVM; civil applications; crop yield appraisal; data mining classifier; environmental damage evaluation; growth regulation; image classification algorithms; k-nearest neighbor; land cover classification; land use classification; land use mapping; land use monitoring; mapping products creation; military applications; neural network; radiation monitoring; remote sensing images; soil assessment; support vector mchaines; texture based supervised classification; texture classifier; time complexity; urban planning; Accuracy; Classification algorithms; Data mining; Feature extraction; Image segmentation; Remote sensing; Support vector machines; classification; feature extraction; land use mapping;
Conference_Titel :
TENCON 2013 - 2013 IEEE Region 10 Conference (31194)
Conference_Location :
Xi´an
Print_ISBN :
978-1-4799-2825-5
DOI :
10.1109/TENCON.2013.6718977