Monitoring Land Cover of Google Web Service Images through ECOC and ANFIS Classifiers - Sivagami, KP; Jayanthi, SK; Aranganayagi, S (International Journal of Computer Sciences and Engineering, 2025)
Image classification is one of the most significant applications for remote sensing imagery which is used in a wide range of applications from military to farm development by the government and private agencies. The proposed work focuses on the land use / land cover classification using advanced supervised classification techniques, Error Correcting Output Code (ECOC) multiclass model classifier and Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. The samples of different classes such as, vegetation, quarry, water, wasteland and urban area were collected from different locations, refined, and trained on RGB, Gray and HSV color spaces based on the features mean, standard deviation, energy, contrast, entropy, and homogeneity. In ANFIS classifier when the number of inputs is more, the construction of FIS structure causes excessive propagation of number of rules which leads to memory overhead. Owing to this limitation, the number of features was restricted to mean and standard deviation in HSV and RGB color spaces. Based on the performance measures overall accuracy and kappa coefficient, it has been observed that the ECOC classifier produces better results in RGB color space and hence it has been applied on different locations of Tamil Nadu in Google Maps. From the results it has been proved that the ECOC classifier performs well when the ground cover nature is heterogeneous in nature.