NEURAL NETWORK APPROACHES VERSUS STATISTICAL-METHODS IN CLASSIFICATION OF MULTISOURCE REMOTE-SENSING DATA

被引：613

作者：

BENEDIKTSSON, JA ^{[1
]}

SWAIN, PH ^{[1
]}

ERSOY, OK ^{[1
]}

机构：

[1] PURDUE UNIV,APPLICAT REMOTE SENSING LAB,W LAFAYETTE,IN 47907

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 1990年 / 28卷 / 04期

基金：

美国国家航空航天局;

关键词：

D O I：

10.1109/TGRS.1990.572944

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

Neural network learning procedures and statistical classification methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that the two different approaches have unique advantages and disadvantages in this classification application. © 1990 IEEE

引用

页码：540 / 552

页数：13

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