Introduction to Fuzzy Logic Computer Science Seminar Topic:
This paper is about land cover classification using C. The objects are divided into groups which are called as clusters. Here the objects of the similar type of clusters are grouped together whereas the objects of different objects are clustered separately.
Land cover describes the land surface. It can be natural or semi-natural or can be man-made.
They are directly observable. The reason for knowing about the land coverage maps is for giving the brief idea about the natural and also the built resources.
The Fuzzy C is a method of clustering which allows one data to belong to two or more clusters and is frequently used in image segmentation and pattern recognition.
A new reformed fuzzy C means technique has been proposed for land cover classification. The satellite image segmentation and land cover classification by RFCM are compared with that of FCM and PFCM on the quality measures PSNR, compression ratio, and execution time and Kappa coefficient.
The application which is developed also tested with various pictures and the results is proved to be successful. The program is also tested for its consistency and its reliability. This work is useful for land cover mapping. Land cover maps are essential inputs to environmental and land use planning at local, regional, and national levels. They are being used directly in resource inventories like woodland census, state of the environment reporting like distribution establishment, extent and quality in case of the key habitats. They also act as input to the development and monitoring of a range of land use and conservation policies.
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