A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
AccuracyAssessment of Supervised and Unsupervised Image Classification of Fused Satellite Images
Remote sensing techniques have been extensively utilized for recognition of land use and land cover structures. Land evidence can be definitely composed by classification of satellite images in the perspective of their practice. In this paper study area has been classified into five classes i.e. vegetation, agriculture, water body, open area and urban land by classification of fused images obtained from various fusion techniques. The spatial and spectral determinations of various satellite images make availableimprovedevidence with the encouragementof imageprocessing and image fusion of both multispectral and spatial images. The input images fused together are multispectral image and panchromatic images obtained from IRS-1D satellite utilizing LISS III. Matlab 10.0 software has been used for image processing, fusion and classification of the images. The Principal Component Analysis (PCA), wavelet transform, fuzzy and neuro fuzzy techniques arehave been used for image fusion. The resultant images have been classified using the supervised and unsupervised classification techniques;decision tree classifier and K-Meansalgorithms and evaluationconcerning them in standings of their accuracy. Keywords:fusion,classification,accuracy,PCA,wavelet,neuro fuzzy
AccuracyAssessment of Supervised and Unsupervised Image Classification of Fused Satellite Images
Remote sensing techniques have been extensively utilized for recognition of land use and land cover structures. Land evidence can be definitely composed by classification of satellite images in the perspective of their practice. In this paper study area has been classified into five classes i.e. vegetation, agriculture, water body, open area and urban land by classification of fused images obtained from various fusion techniques. The spatial and spectral determinations of various satellite images make availableimprovedevidence with the encouragementof imageprocessing and image fusion of both multispectral and spatial images. The input images fused together are multispectral image and panchromatic images obtained from IRS-1D satellite utilizing LISS III. Matlab 10.0 software has been used for image processing, fusion and classification of the images. The Principal Component Analysis (PCA), wavelet transform, fuzzy and neuro fuzzy techniques arehave been used for image fusion. The resultant images have been classified using the supervised and unsupervised classification techniques;decision tree classifier and K-Meansalgorithms and evaluationconcerning them in standings of their accuracy. Keywords:fusion,classification,accuracy,PCA,wavelet,neuro fuzzy
AccuracyAssessment of Supervised and Unsupervised Image Classification of Fused Satellite Images
Babu, Ch Ramesh (author) / Seetha, M . (author) / Rao, D.Srinivasa (author) / Prasad, MHM Krishna (author)
2017-04-30
doi:10.26483/ijarcs.v8i1.2876
International Journal of Advanced Research in Computer Science; Vol 8, No 1 (2017): JANUARY - FEBRUARY 2017; 169-173 ; 0976-5697 ; 10.26483/ijarcs.v8i1
Article (Journal)
Electronic Resource
English
SEM Algorithm and Unsupervised Statistical Segmentation of Satellite Images
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