Feature Selection for Urban Land-Cover Classification using Landsat-7 ETM+ Data

Prakash C. R., Sridevi B., Asra M., Dwivedi R.S.

Abstract


We report here the results of a study carried out to reduce the dimensionality of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) digital data by principal component analysis, and generating a band triplet with maximum optimum index factor (OIF) value for developing land-cover map over a metropolitan city through Gaussian maximum likelihood algorithm. The performance of the thematic maps, thus generated from these three data sets, was done by a systematic accuracy assessment. Results indicate that a band triplet (ETM+ band 2, 4 and 5) with the maximum optimum index factor (OIF) value, and an overall accuracy of 97.5% and a kappa accuracy value of 0.9656 outperformed other two datasets viz. original 6-reflective bands of ETM+ data and a PC triplet (PC1, PC2 and PC3). The overall and a kappa accuracies values for original 6-reflective bands of ETM+ data have estimated as 96.7% and 0.9541, respectively. For a PC triplet (PC1, PC2 and PC3) these values are 94.17% and 0.9210, respectively indicating thereby the potential of transformed data in generating improved land cover information of an urban environment. The methodology and the results are discussed in detail.


Keywords


Enhanced Thematic Mapper Plus (ETM+); Optimum Index Factor (OIF); Principal Component Transform; Classification Accuracy

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