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粗糙集理论进行遥感图像监督分类的样本质量评价的研究

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Exploring the Sample Quality Using Rough Sets Theory for the Supervised Classification of Remotely Sensed Imagery
In the supervised classification process of remotely sensed imagery, the quantity of samples is one of the important factors affecting the accuracy of the image classification as well as the keys used to evaluate the image classification. In general, the samples are acquired on the basis of prior knowledge, experience and higher resolution images. With the same size of samples and the same sampling model, several sets of training sample data can be obtained. In such sets, which set reflects perfect spectral characteristics and ensure the accuracy of the classification can be known only after the accuracy of the classification has been assessed. So, before classification, it would be a meaningful research to measure and assess the quality of samples for guiding and optimizing the consequent classification process. Then, based on the rough set, a new measuring index for the sample quality is proposed. The experiment data is the Landsat TM imagery of the Chinese Yellow River Delta on August 8th, 1999. The experiment compares the Bhattacharrya distance matrices and purity index ⊿ and ⊿x based on rough set theory of 5 sample data and also analyzes its effect on sample quality.

supervised classificationmeasuring the sample qualityrough set

葛咏、白鹤翔、李三平、李德玉

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Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, 11A Datum Road, Beijing 100101, China

supervised classification measuring the sample quality rough set

国家自然科学基金Open Research Fund from State Key Laboratory of Remote Sensing Science国家高技术研究发展计划(863计划)

40671136LRSS06102006AA12Z215

2008

地球空间信息科学学报(英文版)
武汉大学（原武汉测绘科技大学）

地球空间信息科学学报(英文版)

影响因子：0.207
ISSN：1009-5020
年,卷(期)：2008.11(2)
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