An evaluation of fuzzy classifications from IRS 1C LISS III imagery: a case study

被引：29
|
作者：
Shalan, MA
Arora, MK
Ghosh, SK
机构：
[1] Aston Univ, Sch Engn & Appl Sci, Birmingham B4 7ET, W Midlands, England
[2] Indian Inst Technol, Dept Civil Engn, Roorkee 247667, Uttar Pradesh, India
来源：
INTERNATIONAL JOURNAL OF REMOTE SENSING | 2003年 / 24卷 / 15期
关键词：
D O I：
10.1080/0143116031000094791
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
Remote sensing data are frequently used to produce crisp and fuzzy classifications for land cover applications. Fuzzy approaches are attractive for classification of images dominated by mixed pixels. Recently, fully-fuzzy classification that can incorporate mixed pixels in all the three stages of a supervised classification has been recommended. In this Letter, the results of a case study on fully-fuzzy classification of Indian Remote Sensing (IRS) 1C Linear Imaging Self Scanning Sensor (LISS) III imagery are reported. The results illustrate that fully-fuzzy classification produces more accurate land-cover mapping than the conventional crisp classification. For instance, the areal extents of three dominant classes (i.e. agriculture, forest and grass) obtained from fully-fuzzy classification differ by only 13% from the actual areal extents, compared to 34% difference in area observed from crisp classification.
引用
收藏
页码：3179 / 3186
页数：8
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