Image Classification for Different Land Use and Land Covers Using Fuzzy Logic for the Improvement of Accuracies

The aim of present research is to classify the satellite images of Ranchi area using fuzzy logic for different land use and land covers. An IRS-LISS III (Linear Imaging Self Scanning Sensor) image has been used for classification. Fuzzy logic is relatively a new theory. Now, fuzzy logic is widely used in the classification of remotely sensed images, for various land use and land cover classes. Classification of images includes pervious and impervious categories. Pervious categories contain mainly standing water bodies, natural vegetation and agricultural lands. Impervious categories contain dense built-up, moderate built-up and low density built-up area. The images of Ranchi area has been classified using standard maximum likelihood (ML) as well as fuzzy techniques using supervised method of classification using ERDAS IMAGINE 9.1. After classification of images, producer’s accuracy, user’s accuracy, overall accuracy and kappa coefficient values have been calculated with the help of confusion / error matrix. Result shows that in pervious category, standing water body exhibits highest accuracy (100%), then natural vegetation and agricultural land exhibits lowest accuracy. Standing water exhibits highest accuracy due to more clear pixels. Among the impervious categories, low density built-up area exhibits highest producer’s accuracy due to small area, dense built-up has second highest and moderate built-up has lowest producer’s accuracies. Comparison among accuracies have been done for both techniques and it is observed that the fuzzy logic is a better classification methodology than the standard ML method because overall accuracy and kappa value are higher for fuzzy classified images.