基于GF-1卫星数据的面向对象的民勤绿洲植被分类研究

摘要/Abstract

摘要： 以民勤绿洲为研究区，以GF-1遥感影像为数据源，采用面向对象的分类方法，结合分层技术，对影像逐级进行分类，以获取植被信息。根据归一化植被指数（NDVI）阈值区分植被与非植被，分割尺度为10；使用归一化水体指数（NDWI）阈值提取非植被中的水体，分割尺度为35；利用野外采样点获取的训练样本，将植被进一步分为耕地、林地和草地，分割尺度为25。总体分类精度达到83.02%，Kappa系数为0.745 1，比较基于象元的监督分类，其总体分类精度为69.37%，Kappa系数为0.497 0，表明面向对象的分类方法在干旱区绿洲植被信息的提取上较传统的基于象元的分类方法更有优势，分类精度更高。

关键词: GF-1, 民勤绿洲, 面向对象, 监督分类

Abstract: In this study,Minqin oasis,Gansu Province,China was taken as the research area. Based on the GF-1 remote sensing image and wild typical sampling data,the object-oriented classification method was used to classify the images in order to obtain the vegetation spatial distribution information. According to the specific characteristics of the study area,the segmentation scale 5 was taken as the initial value,and the segmentation experiment was carried out with the increase of step length from 5 to 100. In the first layer,the Normalized Difference Vegetation Index(NDVI)was used to distinguish vegetation and non-vegetation,and the threshold value was 0.16. In this layer,In order to distinguish the smaller vegetation,the segmentation scale was 10. In the second layer,the Normalized Difference Water Index(NDWI)was used to extract the water in non-vegetation,and the threshold value was -0.02. According to the size,shape and other characteristics of water,the segmentation scale was 35. In the third layer,with the training samples obtained from field sampling survey,the nearest neighbor classification method was used to extract the vegetation from the cultivated land,woodland and grassland. In this layer,the segmentation scale was 25. The accuracy of the classification results was evaluated by the sampling data and the Google map data. The overall accuracy was 83.02%,and kappa coefficient was 0.745 1,compared with supervised classification of the pixel-based method,whose overall accuracy was 69.37% and kappa coefficient was 0.497 0. It indicates that the object-oriented classification method has more advantages than the supervised classification of the pixel-based method in extracting the vegetation information of arid area. In this paper,according to the rule of different features using different segmentation scales,the hierarchical classification could not only decrease the occurrence of over-segmentation and under-segmentation,but also improve the classification accuracy and shorten the time. It was a new attempt in the study of vegetation classification in arid regions.

Key words: GF-1, Minqin oasis, object-oriented, supervised classification

中图分类号:

TP79

张华, 张改改, 吴睿. 基于GF-1卫星数据的面向对象的民勤绿洲植被分类研究[J]. 干旱区地理, 2017, 40(4): 831-838.

ZHANG Hua, ZHANG Gai-gai, WU Rui. Object-based vegetable classification based on GF-1 imagery in Minqin Oasis[J]. , 2017, 40(4): 831-838.

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