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Block Truncation Coding (BTC) approach in Multispectral Remote Sensing Image Classification

Pravada S. Bharatkar, Rahila Patel


The thirst of better and faster image classification is increasing and earned enormous attention due to advent of modern day applications involving image base information. Developing computationally efficient algorithm for image classification without compromising the classification accuracy is of primary importance. In classification of a multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy. Hence, there is need to improve the existing image classification algorithm. In this study, we recommended that the incorporation of Block Truncation Coding (BTC) approach in the existing algorithm can be used for improving the classification accuracy. To prove this concept, the present study has been carried out for exploring the BTC based novel classification method. The procured IRS 1D LISS III satellite image from NRSC, Hyderabad is reconstructed with BTC approach. Five themes of land use/land cover classes (LULC) are considered and ground verification was carried out to verify and assess the accuracy of classification. The various supervised classification and unsupervised clustering methods were employed on original and reconstructed image and compared the performance in terms of overall accuracy and kappa statistics. The results show that the maximum likelihood supervised classification method gives better overall accuracy and kappa statistics to classify the reconstructed (BTC) RS image. Hence, the study elucidated that the reinforcement of BTC approach in the existing classification methods can be a novel classification approach to derive an efficient and accurate LULC map.


Remote sensing, classification algorithm, BTC, satellite image, land use or land cover

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