Contextual classification in image analysis: an assessment of accuracy of ICM

被引:15
作者
Arbia, G
Benedetti, R
Espa, G
机构
[1] Univ Pescara, Dept Quantitat Methods & Econ Theory, I-65127 Pescara, Italy
[2] ISTAT, I-00142 Rome, Italy
[3] Univ Trento, Inst Stat & Operat Res, I-38100 Trent, Italy
关键词
autologistic model; Bayesian classification; global accuracy; image classification; Markov random fields; random fields simulation; spatial pattern of classification errors;
D O I
10.1016/S0167-9473(98)00104-2
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper considers the performances of the ICM image classification technique contrasted with the maximum likelihood ordinary discriminant analysis (ML). The latter technique is the most widely used in an applied context by space agencies and remote sensing units. The two methods are compared in terms of the global accuracy produced and in terms of the spatial continuity properties of classification errors. ICM outperforms ML in most experimental cases in terms of the global accuracy produced. However, in some instances, it has a more marked tendency to produce classification errors that are short-distance correlated. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:443 / 455
页数:13
相关论文
共 22 条
[1]  
Arbia G., 1996, Geogr. Syst., P123
[2]  
BENEDETTI R, 1993, STAT APPL, V5, P217
[3]  
BESAG J, 1974, J ROY STAT SOC B MET, V36, P192
[4]  
BESAG J, 1986, J R STAT SOC B, V48, P259
[5]  
BESAG JE, 1983, B INT STAT I, V50, P422
[6]  
Cliff A, 1981, Spatial processes :models and applications
[7]   MARKOV RANDOM FIELD TEXTURE MODELS [J].
CROSS, GR ;
JAIN, AK .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1983, 5 (01) :25-39
[8]  
Dubes R.C., 1989, J APPL STAT, V16, P131, DOI DOI 10.1080/02664768900000014
[9]  
*ERD INC, 1994, ERD FIELD GUID
[10]   MONTE-CARLO CALCULATION OF PHASE SEPARATION IN A 2-DIMENSIONAL ISING SYSTEM [J].
FLINN, PA .
JOURNAL OF STATISTICAL PHYSICS, 1974, 10 (01) :89-97