Classification of remote sensing images based on the fusion of spatial relationship

doi:10.6046/gtzyyg.2017.03.02

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Abstract In order to overcome the disadvantages of the classification method based on spectral and texture features, the authors put forward a classification method based on the fusion of spatial relationship in this paper. Single object probability was built by G statistic after image object feature was extracted by histogram. The neighborhood object probability was described by land cover adjacency probability which was calculated by iterative statistics method. The joint probability of the object was built by the weighted combination of single object probability and neighborhood object probability. The classification result of the image was obtained according to the maximum a posteriori. The experimental results based on QuickBird image show that the proposed method can improve the classification accuracy compared with the traditional classifier using spectral and texture features. The overall classification accuracy and kappa coefficient are increased by 1.5% and 2.1%, respectively.

Keywords Shendong mining area surface vegetation fractional vegetation coverage(FVC) dynamic monitoring trend analysis

Issue Date: 15 August 2017

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	LIU Ying
	HOU Enke
	YUE Hui

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LIU Ying,HOU Enke,YUE Hui. Classification of remote sensing images based on the fusion of spatial relationship[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 10-16.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.03.02 OR https://www.gtzyyg.com/EN/Y2017/V29/I3/10

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