Some issues in contextual fuzzy c-means classification of remotely sensed data for land cover mapping

被引：3
|
作者：
Dutta, A. [1 ]
Kumar, A. [1 ]
Sarkar, S. [2 ]
机构：
[1] NRSC, Indian Inst Remote Sensing, Dehra Dun 248001, Uttar Pradesh, India
[2] Univ Calcutta, Dept Geog, Kolkata 700073, India
来源：
JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING | 2010年 / 38卷 / 01期
关键词：
Contextual information; Markov Random Field; Metropolis Algorithm; Gibbs Samples; IMAGERY;
D O I：
10.1007/s12524-010-0002-1
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Earlier for the hard classification techniques contextual information was used to improve classification accuracy. While modelling the spatial contextual information for hard classifiers using Markov Random Field it has been found that Metropolis algorithm is easier to program and it performs better in comparison to the Gibbs sampler. In the present study it has been found that incase of soft contextual classification Metropolis algorithm fails to sample from a random field efficiently and from the analysis it was found that Metropolis algorithm is not suitable for soft contextual classification due to the high dimensionality of the soft outputs.
引用
收藏
页码：109 / 118
页数：10
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