Identification of coastal reclamation from GF-1 imagery using ensemble classification strategy

doi:10.6046/gtzyyg.2017.01.22

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Abstract

The coastal reclamation is an important way for people to access marine resources. Monitoring the coastal reclamation changes is an important task in coastal zone management and coastal zone evolution study. However, the coastal reclamation feature is complex, and it is difficult for remote sensing techniques to efficiently monitor reclamation. In this paper, the authors propose an ensemble classification algorithm for identifying four categories of reclamation using GF-1 imagery. The ensemble classification is constructed based on minimum distance algorithm and 10 features from manually extracted image objects. The 10 features include four mean features of each object in the four bands of GF-1 imagery respectively, mean value of the four mean features, object size, object perimeter, external rectangular area, ratio of object area, external rectangular area, ratio of object perimeter and object area. The proposed method was extensively tested by using two GF-1 images from 2013 and 2014. The results show that the highest accuracy of single feature model is up to 82.03%, and the accuracy of spectral features based ensemble model and that of the spatial features based ensemble model are 63.28% and 87.50% respectively, and the accuracy of full feature based ensemble model is 80.47%. This study provides a useful solution for monitoring the coastal reclamation.

Keywords three-line-array images image matching SIFT features correlation coefficient matching pyramid images

TP79

Issue Date: 23 January 2017

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	DU Shouji
	ZOU Zhengrong
	ZHANG Yunsheng
	ZHANG Minglei

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DU Shouji,ZOU Zhengrong,ZHANG Yunsheng, et al. Identification of coastal reclamation from GF-1 imagery using ensemble classification strategy[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 143-148.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.01.22 OR https://www.gtzyyg.com/EN/Y2017/V29/I1/143

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