Abstract
Fuzzy inference method is introduced to classify landcover using LANDSAT MSS data. The proposed algorithm is intended for use to estimate area components of landcover types which are present in a spacial segment of the order of 0.5 to 1 km2. Divided into two hundreds 750m×750m square segments is the study area whose half portion is utilized as the training area and the other half as the test area. Average of the each of the four band MSS data on a segment in the training area is assumed as fuzzy number which makes up an assumption of a fuzzy production rule while the area of each of the landcover types in the segment forms a conclusion of that rule which could be crisp or fuzzy. Widely conducted are the experiments which reveal that; (1) the input MSS data should also be made fuzzy number, (2) the area used in the conclusion should be crisp, (3) 60% rules selected randomly from the provided production rules can produce satisfying estimates, (4) the developed algorithm outperforms the discriminant function algorithm by 33% based on root mean squared error index and is robust against noises lurking in the MSS data.
