摘要
针对东江流域地物斑块破碎、湖泊河流众多等因素影响其地物分类精度的问题,该文以GF-1遥感影像为数据源,采用面向对象的分类方法,结合模糊分类和CART决策树分类法获取研究区土地利用分类信息。根据近红外波段均值的模糊范围(480~2 200)选择模糊小于隶属函数对水体与非水体进行区分,近红外波段均值小于480确定为水体,大于2 200确定为非水体;在水体类别中,采用长宽比指数模糊范围(1.53~4.32)调用模糊大于函数对河流与水库进行了区分,长宽比指数小于1.53确定为水库,大于4.32确定为河流;在非水体类别中,采用归一化植被指数NDVI(normalized difference vegetation index)特征值模糊范围(0.21~0.62)调用模糊大于函数区分植被与非植被,NDVI指数小于0.21确定为非植被,大于0.62确定为植被,最后采用面向对象的CART决策树分类法分出河流、水库、园地、林地、耕地、灌草地、未利用地、建设用地。与极大似然分类法、非监督分类法应用到GF-1遥感影像相比,基于面向对象的CART决策树分类方法的效果最好,总体分类精度高达93.27%,Kappa系数高达0.92。该方法可以作为东江流域获取较高土地利用信息的有效方法,为研究流域生态环境变化提供更准确的数据支持。
We are aimed at the problem of the low classification accuracy of the land surface of the Dongjiang River Basin due to the patch breaking of the Dongjiang River Basin and the numerous lakes and rivers which are found everywhere in the river basin. At present, the data for the Dongjiang River Basin are mostly Landsat TM/ETM images, and the spatial resolution is low, so the multi-view image splicing is needed. The Dongjiang River Basin is in the south China where it is cloudy and rainy. The Landsat TM/ETM image with less cloud is insufficient, and it is difficult to ensure the time consistency, which influences the information extraction effect; and the cost of high spatial resolution image is high, so it is difficult to be applied in the whole basin range. The GF-1 remote sensing image is used as the data source in this paper, and we try our best to use object oriented classification method combined with fuzzy classification and CART（classification and regression trees） decision tree classification method to obtain land use classification information of Dongjiang River Basin by doing the experiments. We spare no effort to attempt a method that can make the classification of Dongjiang River Basin accurately. We read a lot of relevant taxonomy and refer to many experiments, and then launch a classification scheme. We use the software of eCognition 9.0 to complete the process of fuzzy classification and CART decision tree classification, and also we use several kinds of software such as ArcGIS 10.1, ENVI 5.3, SP1, and Qmosaic 6.0 to help do this work. Also we will combine true color remote sensing images of GF-1 to read the classification process visually. First of all, we choose the fuzzy range from 480 to 2 200, which is based on the mean of the near-infrared band, and combined with the true color remote sensing images of GF-1. When the mean value of near-infrared band is less than 480 by the experiment, it is identified as water body, and when the mean value of near-infrared band is more than 2 200, it is identified as non-water. We choose fuzzy less than membership function to distinguish between water and non-water. Then in water category, the length-width ratio index fuzzy range from 1.53 to 4.32 is used to distinguish the river from the reservoir by using fuzzy greater than function. Similarly, we observe the true color remote sensing images of GF-1. When the index of length-width ratio is less than 1.53, it is identified as a reservoir, and when the index of length-width ratio is more than 4.32, it is identified as a river. In the non-water category, we try to use the fuzzy range of the normalized vegetation index NDVI （normalized difference vegetation index） characteristic value （i.e. from 0.21 to 0.62） to distinguish the vegetation and non-vegetation. When the NDVI index is less than 0.21, it is identified as non-vegetation; when the NDVI index is more than 0.62, it is identified as vegetation. Finally, we use CART decision tree classification method based on samples to distinguish river, reservoir, garden plot, woodland, farmland, grassland, unused land and construction land. Compared with the maximum likelihood classification method and the unsupervised classification method applied in GF-1 remote sensing images, the object oriented CART decision tree classification based on sample method has the best classification effect, whose overall classification accuracy is up to 93.27%, and the Kappa coefficient is up to 0.92. This method can be used as an effective method to obtain higher land use information in Dongjiang River Basin, and it also can provide more accurate data for the study of ecological environment changes in the watershed.
作者
李恒凯
吴娇
王秀丽
Li Hengkai;Wu Jiao;Wang Xiuli(College of Architecture and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China;College of Economic Management, Jiangxi University of Science and Technology, Ganzhou 341000, China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2018年第10期245-252,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
江西省自然科学基金(编号:20161BAB206143)
江西省教育厅科技课题(编号:GJJ150659)
江西省社会科学规划课题(17YJ20)
江西省高校人文社科课题(JC17111)
关键词
遥感
土地利用
GF-1
东江流域
分类
面向对象的CART决策树分类
remote sensing
land use
GF-1
Dongjiang River Basin
classification
Object-oriented classification and regression trees
