Abstract :
In the conventional remote sensing supervised classification, training informalion and classification results are represented in a one - pixel - one - class method. Class mixture cannot be taken into consideration in training classifier and in- determining pixels membership. The expressive limitation has reduced the classification accuracy level and led to the poor extraction of information. This paper describes a fuzzy supervised classification method in which geographical information is represented as fuzzy sets. The algorithm consists of two major steps: The estimate of fuzzy parameters from fuzzy training data, and fuzzy partitions of spectral space. Partial membership of pixels allows component cover classes of mixed pixels to be identified and more accurate statistical accuracy to be achieved. Results of classifying a landsat TM images are presented and their accuracy is analyzed.
