Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method-Reference-Cited by-同舟云学术

Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method

Published:2013-08 Issue:3-4 Volume:58 Page:573-581
ISSN:0895-7177
Container-title:Mathematical and Computer Modelling
language:en
Short-container-title:Mathematical and Computer Modelling

Author:

Sun Jiabo,Yang Jianyu,Zhang Chao,Yun Wenju,Qu Jieqing

Funder

National High Science and Technology Development Plan (863 Plan) in China

Pre-Development of the high resolution application demonstration system on land resources

Publisher

Elsevier BV

Subject

Computer Science Applications,Modeling and Simulation

Reference27 articles.

1. A fuzzy topology-based maximum likelihood classification;Liu;ISPRS Journal of Photogrammetry and Remote Sensing,2010

2. A rapid method to generate spectral theme classification of Landsat imagery;Shlien;Remote Sensing of Environment,1976

3. Rapid maximum likelihood classification;Bolstad;Photogrammetric Engineering and Remote Sensing,1991

4. Inclusion of prior probabilities derived from a nonparametric process into the maximum-likelihood classifier;Maselli;Photogrammetric Engineering and Remote Sensing,1992

5. Learning Machines: Foundations of Trainable Pattern-Classifying Systems;Nilsson,1965

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