Digital image classification techniques group pixels to represent land cover features. Land cover could be forested, urban, agricultural and other types of features. There are three main image classification techniques. One of them is Unsupervised classification where the outcomes (groupings of pixels with common characteristics) are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorithm the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as wetlands, developed areas, coniferous forests, etc.). For this project, there are 30 samples and we need to class category into 5 classes which is water bodies, forest, vegetation, bare land and urban. Proceed the same step for different algorithm, K-mean and Isodata.
Software Used :
Erdas Imagine 2013
Satellite Image Used :
Result of Unsupervised Classification :
My Report and Procedure of Unsupervised Classification :
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