Design of Classification and Recognition Inference Decider for Soil Remote Sensing
土壤遥感分类识别推理决策器的设计

This paper presents design principle and realizable approach of Classification and Recognition Inference Decider for Soil Remote Sensing (CRID) in arid land. On the basis of non_supervising classification for soils with TM images, the author discusses the reasoning mechanism by combining the direct inference combined with inverse reasoning for soil classification and recognition decision. The author also expresses soil classification and recognition knowledge of expert in soil science using data structure of producing rule linked with frame rule for knowledge expression in the CRID. Furthermore, the author make up the rules of soil classification and recognition with image structural model, and builds decision tree of soil classification in the CRID, and organizes files of decision for soil classifications with typical image case model. With these methods, the author carries out a test research on classification and distinguishing for soil in test region of Fukang Counry, situated on the northern foot of the Tianshan Mountains, Xinjiang Province. And the test result shows that the approach mentioned above has a high reliable precision, and it reclaims a new way for classification and recognition of soil in arid land.