Date Added: Nov 2012
Soft computing techniques are becoming popular in designing real world applications. Researchers are trying to integrate different soft computing paradigms such as fuzzy logic, artificial neural network, genetic algorithms, decision trees etc. to develop hybrid intelligent autonomous classification systems that provide more flexibility by exploiting tolerance and uncertainty of real life situations. The paper reviews soft classification techniques for remotely sensed data. The emphasis is placed on the summarization of major soft classification approaches and the techniques used for RS data classification.