A change detection method for vector map and remote sensing imagery based on object heterogeneity

doi:10.6046/gtzyyg.2018.01.05

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Abstract

In order to realize the automatic change detection with vector map and remote sensing imagery, a change detection method based on the object heterogeneity for vector map and remote sensing imagery is proposed in the paper. Image segmentation under the constraint of vector map was employed to get image objects using marker-based watershed algorithm. The features of the object were extracted by histogram which describes both gray feature and texture feature. The histogram intersection distance was adopted to measure the feature distance. The object heterogeneity was built by the average of the distance between the object and the other objects with the same class in old period. Change/nochange label of the objects can be determined by comparison the object heterogeneity with the heterogeneity threshold of the class which was calculated by Maximum Entropy Principle automatically. Experiments on QuickBird remote sensing images verified the effectiveness of the proposed method ,and the correct rate of the change detection is up to 95%.

Keywords object object heterogeneity image segmentation histogram intersection distance maximum entropy

TP751.1

Issue Date: 08 February 2018

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	Articles by authors

	Liang LI
	Lei WANG
	Kai WANG
	Sheng LI

Cite this article:

Liang LI,Lei WANG,Kai WANG, et al. A change detection method for vector map and remote sensing imagery based on object heterogeneity[J]. Remote Sensing for Land & Resources, 2018, 30(1): 30-36.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.05 OR https://www.gtzyyg.com/EN/Y2018/V30/I1/30

Fig.1 Flowchart of proposed method

Fig.2 Diagram of image segmentation constrained by vector map

Fig.3 Diagram of object histogram features

Fig.4 Diagram of object class heterogeneity

Fig.5 Experimental data

Fig.6 Relationship between change detection accuracy and gray level

Fig.7 Comparison of change detection results by using different methods

Tab.1 Comparison of change detection results by using two methods(%)

Tab.2 Confusion matrix of change detection results by using proposed method

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