Abstract
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.
Highlights
Image classification is a process to assemble groups of identical pixels found in remotely sensed data into classes that match the required categories of user by comparing pixels to one another and those of known identity (Palaniswani et al, 2006)
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 maximum likelihood (ML) method because overall accuracy and kappa value are higher for fuzzy classified images
A traditional hard classification technique does not take into account this continuous change in land cover classes and only assigns the single class level which dominates in a pixel, it leads to loss of information (Kumar et al, 2007)
Summary
Image classification is a process to assemble groups of identical pixels found in remotely sensed data into classes that match the required categories of user by comparing pixels to one another and those of known identity (Palaniswani et al, 2006). Mixed pixels occur because the pixel size may not be fine enough to capture detail on the ground necessary for specific applications (Campbell, 1984) They may occur where the ground properties, such as vegetation and soil types, vary continuously (Wood & Foody, 1993). Standard classification methods such as maximum likelihood technique is sometimes not able classify mixed pixels accurately. In the remote sensing Fuzzy C-Means (FCM) clustering algorithm has been widely used to classify satellite images with vague land cover classes (Zhang & Foody, 1998), which decompose the pixel into its class proportions. Earlier in contextual FCM classification of remotely sensed data, it was found that the contextual information could be useful to map the real world phenomena more accurately (Dutta et al, 2008)
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