FUZZY APPLICATION IN GIS

Satellite images are the most economical way of getting data for different times. The multitude of existing software helps getting information from satellite images also in manipulating the information. The approach used in this study to classify satellite images and change detection is based on the fuzzy logic approach. Change detection is a major task in digital image processing. This task helps in many applications as mentioned above. Change detection is done digitally using two satellite images. If the images are classified and then compared for the change, it is called Post Classification Comparison (PCC). If this is done before classification, using only geo-referenced images, it is called Pre Classification Change detection. This is purely based on the digital number of a pixel in different bands. But simple methods of classification of images and change detection suffer from poor accuracy. To detect the change accurately, we need the accurate classification. Results of traditional classification methods assign crisp boundaries to classes. Crisp class assignment is often inappropriate in geographical and Remote Sensing sciences. This is because of the uncertainties present in the geographical objects. To quantify uncertainties, it is advisable to use fuzzy approach. The past few years have witnessed a rapid growth in the number and variety of applications of fuzzy logic.
The applications range from consumer products such as cameras, washing machines, and microwave ovens to industrial process control, medical instrumentation, decision-support systems, and in remote sensing of course.