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高分辨率遥感影像耕地分层提取方法
引用本文:陈 杰,陈铁桥,刘慧敏,梅小明,邵权斌,邓 敏.高分辨率遥感影像耕地分层提取方法[J].农业工程学报,2015,31(3):190-198.
作者姓名:陈 杰  陈铁桥  刘慧敏  梅小明  邵权斌  邓 敏
作者单位:1. 中南大学地球科学与信息物理学院地理信息系,长沙 410083;,1. 中南大学地球科学与信息物理学院地理信息系,长沙 410083; 2. 中国科学院西安光学精密机械研究所遥感与智能信息系统研究中心,西安 710119;,1. 中南大学地球科学与信息物理学院地理信息系,长沙 410083;,1. 中南大学地球科学与信息物理学院地理信息系,长沙 410083;,1. 中南大学地球科学与信息物理学院地理信息系,长沙 410083;,1. 中南大学地球科学与信息物理学院地理信息系,长沙 410083;
基金项目:国家重点基础研究发展计划(973项目)(2012CB719906);国家863计划主题项目(2012AA121301);国家自然科学基金项目(41201428);中科院光谱成像技术重点实验室开放基金(LSIT201406);中国博士后科学基金项目(2012M511762);测绘遥感信息工程国家重点试验室开放研究基金项目(13R01);地理国情监测国家测绘地理信息局重点试验室开放研究基金项目(2013NGCM03)联合资助。
摘    要:随着城市化建设进程的加快,城郊耕地经常会被开发为建设用地,甚至还会遭受非法占用的危险,这极大威胁了中国粮食安全。该文针对高分辨率遥感影像城郊耕地特点,提出了一种多尺度分层的耕地提取方法。首先,基于归一化植被指数(normalized difference vegetation index,NDVI)约束改进传统Harris角点检测方法得到建筑区概率密度图,并利用最大类间方差(Otsu algorithm,Otsu)分割去除复杂建筑区;然后,利用尺度选择工具(estimation of scale parameter,ESP)分析耕地占主导影像的多尺度分割结果,得到耕地较佳分割尺度并在该尺度下分割整幅影像;进而,利用形状、光谱信息初步检测出耕地对象,选择非建筑区的耕地与建筑区的非耕地样本,训练支持向量机模型并对不确定地物进行分类;最后,依据空间关系进一步判断图像对象,得到城郊耕地最终提取结果。试验结果表明,该方法能较高精度地从城郊区域的复杂背景中提取出不同类型、不同光谱的耕地目标。

关 键 词:遥感  图像分割  支持向量机  角点  耕地  高分辨率遥感影像
收稿时间:2014/10/2 0:00:00
修稿时间:2014/12/28 0:00:00

Hierarchical extraction of farmland from high-resolution remote sensing imagery
Chen Jie,Chen Tieqiao,Liu Huimin,Mei Xiaoming,Shao Quanbin and Deng Min.Hierarchical extraction of farmland from high-resolution remote sensing imagery[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(3):190-198.
Authors:Chen Jie  Chen Tieqiao  Liu Huimin  Mei Xiaoming  Shao Quanbin and Deng Min
Institution:1. Department of Geographic, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;,1. Department of Geographic, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; 2. Remote Sensing and Artificial Intelligence Research Center, Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710019, China;,1. Department of Geographic, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;,1. Department of Geographic, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;,1. Department of Geographic, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; and 1. Department of Geographic, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
Abstract:Abstract: Farmland is the material base of human survival and development. Currently, China faces the serious situation that a large population corresponds to less average arable land during a long term. As Chinese urbanization process has accelerated in recent years, farmland in particular area - suburb is often developed to construction land, and even suffers the risk of being illegally occupied. With the implementation of geography national condition monitoring plan on a national scale, China is in urgent need of the development of efficient extraction and monitoring method of farmland for the protection and rational utilization of farmland. High-resolution remote sensing image contains rich and detailed ground information, and it can accurately reflect the suburb terrain types and their spatial distribution. However, house, road, drainage, tree are mixed with farmland in the high-resolution remote sensing image of suburb, and the suburban grounds' features are very similar in the spectrum, shape and texture characteristics, which leads the extraction of farmland to become very difficult. It is more feasible to extract the farmland from the non-construction area, therefore, the construction area is separated out from the image in the first place. The best segmentation scale suitable for farmland is determined according to the multi-scale segmentation in order to accomplish the extraction of farmland in an object-based approach, and then the whole image is segment in this best scale. Furthermore, the typical samples of farmland and non-farmland are selected to train the support vector machine (SVM) model. After the farmland has been classified via SVM, the spatial distribution relationship between segmented objects is taken into consideration to remove the false alarm objects and offset the omitted objects. Specifically, the proposed method consists of four steps: construction area removing, hierarchical farmland extraction, classification via SVM, and judgment by spatial distribution relationship. As a result, according to the characteristics of the suburban farmland in high-resolution remote sensing image, an automatic farmland extraction method combining multi-scale segmentation and hierarchical recognition is presented in this paper. Firstly, an improved algorithm of Harris corner detection constrained by NDVI is developed to extract the corner, and based on the probability density map of built-up areas, the complex construction areas are separated by using Otsu algorithm (OTSU). Secondly, the estimation of scale parameter (ESP) is used to analyze the parameters for generating the multi-scale segmentation of the non-construction area, and then the optimal ones suitable for extracting farmland are selected to segment the image as a whole. Thirdly, based on three rules for identifying farmland objects, shape and spectrum are intergraded to extract the typical farmland objects from a mass of segmented objects; after the SVM model is trained based on the intact farmland samples and the non-farmland samples in construction areas, this model is used to classify the remaining uncertain ground objects. Finally, the spatial distribution relationship is taken into account to refine the classification results produced by SVM. In this study, high-resolution remote sensing image from QuickBird is used to precisely extract farmland in suburb. In the farmland extraction experiments, correct rate of the proposed method is 80.09% which is 17.88% higher than the object-oriented SVM classification method, and error rate of the proposed method is 12.26% which is 1.30% lower than the object-oriented SVM classification method. The final results indicate that the proposed method can effectively extract farmland with different structures and spectral features from high-resolution remote sensing image of the complex environment of suburban area.
Keywords:remote sensing  image segmentation  support vector machine (SVM)  corner  farmland  high-resolution remote sensing imagery
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