收稿日期: 2016-01-17
修回日期: 2016-03-22
网络出版日期: 2016-07-30
基金资助
国家自然科学基金(No.41101410)资助
Remote Sensing Image Classification for NationalGeomatics Monitoring Based on ConditionalRandom Field
Received date: 2016-01-17
Revised date: 2016-03-22
Online published: 2016-07-30
张春森, 冯晨轶, 崔卫红, 郑艺惟, 孙志伟 . 面向地理国情监测的CRF遥感影像分类[J]. 应用科学学报, 2016 , 34(4) : 397 -404 . DOI: 10.3969/j.issn.0255-8297.2016.04.005
A method of high-resolution remote sensing image classification for national geomatics monitoring based on conditional random field (CRF) is proposed. Different from object-based classification, the proposed method is based on probabilistic graphical models that calculate an energy function composed of potentials. The potentials are defined on the pixel-level and the object-level, and the level between pixels and objects. The energy function can be computed by solving a minimum s-t cut problem using a move making algorithm based on powerful graph cut. Since the classification method fully expresses relationship between pixels and objects, classification errors caused by segmentation is effectively reduced. High-resolution remote sensing images of GW-1 are used in the experiment. The overall classification accuracy and average classification accuracy reached 91.08% and 86.95% respectively, much higher than the results of object-based classification.
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