Liu Shaochuang. Approach of Grass Resources Classification Expert System (GES)[J]. Geomatics and Information Science of Wuhan University, 1994, 19(1): 45-51.
Citation: Liu Shaochuang. Approach of Grass Resources Classification Expert System (GES)[J]. Geomatics and Information Science of Wuhan University, 1994, 19(1): 45-51.

Approach of Grass Resources Classification Expert System (GES)

More Information
  • Received Date: April 05, 1993
  • Published Date: January 04, 1994
  • Remote sensing data is widely used in earth resources investigation and has made a great progress. Unfortunately,because the computer classfication is based on multispectral features of images,the accuracy and realiablity are limitted. To improve the accuracy and realisblity of recognizable classes of sround objects,many people have been striving for a long time and many methods have been prompted. A better way is to simultaneously use many kinds of auxiliary data, such as DTM,geographic features soil types, climate, relief, vertical and regional distribution etc.,with remote sensing image in computer classification. For human interpreter,it is not a very difficult problem. But it is not easy for computer. At the moment there is a tendency to develop Expert System in remote sensing image computer classification. It is an efficient way to solve this problem.The structure of Expert System that uses TM image for gtass resources classification is described. Two ports are included in GES:1)Hish level processing part. This port includes the following modules. Knowledge base, Inference engine, Database, Nature language module, Explain module, Knowledge acquisition module etc.. For inference engine,grey inference theory is introduced to inexacting inference.2)Low level processing part. This part is designed to extract information from image and auxiliary data. Such as DTM,soil types, texture features etc.. The image segmantution and output of classification results are also completed by this part.
  • Related Articles

    [1]HE Huiyou, FANG Jian. Gravity Anomaly Spectrum Analysis Method and Its Application[J]. Geomatics and Information Science of Wuhan University, 2023, 48(12): 2092-2102. DOI: 10.13203/j.whugis20200510
    [2]WU Guiping, XIAO Pengfeng, FENG Xuezhi, WANG Ke. Applying Frequency Spectrum Energy Analysis Theory and Methodto Recognize Objects for Remote Sensing Image[J]. Geomatics and Information Science of Wuhan University, 2013, 38(12): 1465-1469.
    [3]CHEN Zezong, JIN Yan, CHEN Xi, FAN Lingang. Numerical Simulation of Doppler Spectrum of Sea Echo for Microwave Radar[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9): 1048-1051.
    [4]WU Guiping, XIAO Pengfeng, FENG Xuezhi, WANG Ke. Object Recognition for High-resolution Remotely Sensed Imagery Based on Energy in Frequency Domain[J]. Geomatics and Information Science of Wuhan University, 2011, 36(11): 1294-1297.
    [5]LU Jun, YANG Qiangwen. International Wireless Operation and GNSS Spectrum Compatibility Analysis in Band 960MHz to 1610MHz[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1172-1176.
    [6]LIU Xuejun, WANG Yanfang, JIN Bei. A Upscaling Method of Digital Elevation Model with Point Spread Function[J]. Geomatics and Information Science of Wuhan University, 2009, 34(12): 1458-1462.
    [7]ZHOU Qu, YAN Guozheng, WANG Wenxing, XU Jinkui. Cepstrum Analysis Based Parameter Estimation for Defocus Blur Image[J]. Geomatics and Information Science of Wuhan University, 2008, 33(3): 318-321.
    [8]LUO Jia, NING Jinsheng. Establishment and Analysis of the Spectrum Relationship Between Earth Gravity Field and KBR[J]. Geomatics and Information Science of Wuhan University, 2004, 29(11): 951-954,1007.
    [9]HU Qingwu, LI Qingquan. Blur Image Restoration Based on Kalman Motion Model[J]. Geomatics and Information Science of Wuhan University, 2004, 29(9): 796-799.
    [10]Qiu Weigen. Ellipsoid Correction in Least Squares Spectral Combination Method[J]. Geomatics and Information Science of Wuhan University, 1989, 14(4): 27-33.

Catalog

    Article views (831) PDF downloads (180) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return