Abstract
The main focus of this study is to measure accuracy of classification for combined spectral features and textural features of fused images obtained after applying different fusion techniques. Study area is selected with a variety of land cover features so as to understand effect of fusion on different land cover features. IHS, GS, PC, CN and Brovey fusion methods are used. A different layer stacked method has been used to create a composite image of bands 5, 4 and 3 of Landsat8, since they show maximum spectral reflectance variations in the land features present in the selected area of study. Resampling is carried out using Nearest Neighbor and Cubic Convolution, where Nearest Neighbor gave better accuracy of fused image. Classification is performed using spectral features of fused images, textural features of fused images and composite images created using spectral and textural features. Fused images are assessed for distortion using visual analysis and statistical parameters. Classification accuracy is measured using error matrix and it has observed that Neural Net classifier produced better accuracies than other classifiers like Maximum Likelihood, Minimum distance classifier and Spectral Angle Mapper. Land cover and land use (LULC) classification accuracy is enhanced using textural features of fused images.
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Birdi, P.K., Kale, K. (2018). Enhancement of Land Cover and Land Use Classification Accuracy Using Spectral and Textural Features of Fused Images. In: Deshpande, A., et al. Smart Trends in Information Technology and Computer Communications. SmartCom 2017. Communications in Computer and Information Science, vol 876. Springer, Singapore. https://doi.org/10.1007/978-981-13-1423-0_33
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DOI: https://doi.org/10.1007/978-981-13-1423-0_33
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