地理科学 ›› 2002, Vol. 22 ›› Issue (1): 72-78.doi: 10.13249/j.cnki.sgs.2002.01.72

• 论文 • 上一篇    下一篇

土壤类型遥感识别推理决策器研究

付炜   

  1. 1. 燕山大学信息学院通信与电子工程系, 河北, 秦皇岛, 066004;
    2. 新疆大学电子信息与工程学院, 新疆, 乌鲁木齐, 830046
  • 收稿日期:2001-01-03 修回日期:2001-06-21 出版日期:2002-01-20 发布日期:2002-01-20
  • 基金资助:
    国家自然科学基金资助项目(批准号69662001).

Research on Inference Decider for Recognition of Soil Classification Remote Sensing

FU Wei   

  1. 1. Department of Communication & Electronic Engineering of Information Institute, Yanshan University, Qinhuangdao, Hebei 066004;
    2. Electronic Information & Engineering Institute, Xinjiang University, Urumqi, Xinjiang 830046
  • Received:2001-01-03 Revised:2001-06-21 Online:2002-01-20 Published:2002-01-20

摘要: 介绍了干旱区土壤类型遥感识别推理决策器的设计原理与实现方法.在用TM遥感图像对土壤类型进行非监督分类的基础上,建立了正向推理与逆向推理相结合的推理机制,对土壤类型进行分类识别决策.用知识表示的产生式规则与框架式规则相结合的数据结构表示土壤学专家的土壤分类识别知识.用像结构模式建立了土壤分类识别的规则,构造了土壤分类判决树,并用典型像例模式进行了各类型土壤判据文件的组织.用该方法对新疆天山北麓阜康试验区的土壤分类识别进行了试验研究.结果表明,该方法分类精度可靠,为干旱区土壤分类识别开辟了一条新的途径.

Abstract: This paper presents design principle and realizable approach for Inference Decider for Recognition of Soil Classification Remote Sensing(IDRS) in arid land. On the basis of non-supervising classifying for soil classifications with TM remote sensing images, the author builds reasoning mechanism of direct inference combined with contrary reasoning for soil classification and recognition decision. The author also expresses soil classification and recognition knowledge of expert in soil with data structure of producing rule linking with frame rule for knowledge expression in the IDRS. Furthermore, the author structures rules of soil classification and recognition with image structure model, and builds decision tree of soil classification in the IDRS, and organizes file of decision for soil classifications with typical image case model. With the means, the author makes a test research on classification and distinguishing for soil in test region of Fukang County, situated on the northern foot of the Tianshan Mountains, Xinjiang Region. And the test result shows that the approach mentioned above is a high reliable precision, and it reclaims a new way for classification and recognition of soil in arid land.

中图分类号: 

  • S127