Data fusion is a powerful tool for the merging of multiple sources of information to produce a better output as compared to\nindividual source. This study describes the data fusion of five land use/cover types, that is, bare land, fertile cultivated land, desert\nrangeland, green pasture, and Sutlej basin river land derived fromremote sensing. A novel framework formultispectral and texture\nfeature based data fusion is designed to identify the land use/land cover data types correctly.Multispectral data is obtained using a\nmultispectral radiometer, while digital camera is used for image dataset. It has been observed that each image contained 229 texture\nfeatures, while 30 optimized texture features data for each image has been obtained by joining together three features selection\ntechniques, that is, Fisher, Probability of Error plus Average Correlation, and Mutual Information. This 30-optimized-texturefeature\ndataset is merged with five-spectral-feature dataset to build the fused dataset. A comparison is performed among texture,\nmultispectral, and fused dataset using machine vision classifiers. It has been observed that fused dataset outperformed individually\nboth datasets. The overall accuracy acquired using multilayer perceptron for texture data, multispectral data, and fused data was\n96.67%, 97.60%, and 99.60%, respectively.
Loading....