Classification of high spatial resolution remotely sensed images by temporal feature fusion

doi:10.6046/gtzyyg.2016.02.15

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Abstract

In order to make full use of the vector data in the historical period and the temporal relationship of the feature classes, the authors propose a classification method based on temporal feature fusion for high spatial resolution remotely sensed imagery in the paper. Image objects are generated using subdivision based on the vector data in the historical period and the remotely sensed imagery in the present period. SVM algorithm is adopted to get the initial class and posterior probability with a single period of the object. Class transition probabilities for description of temporal feature are calculated according to the class of the image objects in the historical and present periods. The iterative method is employed to get the final classification results after weighted combination of the posterior probability with a single period and the transition probability of the image objects. The experiment on QuickBird imagery shows the proposed method can exploit the temporal feature effectively and improve the accuracy of the image classification.

Keywords UAV image CSIFT feature feature matching point homography matrix random sample consensus(RANSAC)

TP79

Issue Date: 14 April 2016

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	GENG Juan
	HE Chenglong
	LIU Xianxin

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GENG Juan,HE Chenglong,LIU Xianxin. Classification of high spatial resolution remotely sensed images by temporal feature fusion[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 91-98.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.02.15 OR https://www.gtzyyg.com/EN/Y2016/V28/I2/91

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