面向地理国情监测的CRF遥感影像分类

doi:10.3969/j.issn.0255-8297.2016.04.005

Abstract

Abstract:

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.

Key words: national geomatics monitoring, conditional random field (CRF), image classification, land cover information extraction

CLC Number:

TN911.73

ZHANG Chun-sen, FENG Chen-yi, CUI Wei-hong, ZHENG Yi-wei, SUN Zhi-wei. Remote Sensing Image Classification for NationalGeomatics Monitoring Based on ConditionalRandom Field[J]. Journal of Applied Sciences, 2016, 34(4): 397-404.

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Remote Sensing Image Classification for NationalGeomatics Monitoring Based on ConditionalRandom Field

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