Parallel fuzzy inference system for large volumes of remote sensing data

In the pattern recognition on the large volume of satellite images, the inference time is much increased. In the remote sensing data having 4 wavebands, 778 training patterns are learned. Each land cover pattern is classified by using 159900 patterns including trained patterns. In the fuzzy classification, 778 fuzzy rules are generated. Each fuzzy rule has 4 fuzzy variables in the condition part. Therefore, high performance parallel fuzzy inference architecture is needed. In this paper, we propose a novel parallel fuzzy inference system on a T3E parallel computer. In this, fuzzy rules are distributed and executed simultaneously. The ONE-To-ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL-TO-ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This system can be used in a large expert system that has many inference variables in the condition and the consequent part