| 基于多特征决策树的建设用地信息提取 |
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| 引用本文： | 饶萍,王建力,王勇.基于多特征决策树的建设用地信息提取[J].农业工程学报,2014,30(12):233-240. |
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| 作者姓名： | 饶萍 王建力 王勇 |
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| 作者单位： | 1. 西南大学地理科学学院，重庆 400715; 2. 毕节学院生态工程学院，毕节 551700;;1. 西南大学地理科学学院，重庆 400715;;1. 西南大学地理科学学院，重庆 400715; |
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| 基金项目： | 中央高校基本科研业务费专项资金项目（XDJK2011C089）；国家自然科学基金项目（40971122） |
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| 摘 要： | 城乡交接带的土地利用/覆盖类型兼具城镇和农村的典型特征。为了解决土地覆盖类型复杂、存在"同谱异物"现象的西部山区环境中建设用地信息难以精确提取的问题,该文提出一种包含多个特征节点的决策树分类法,该方法以Landsat-8影像为主要数据源,以决策树分类法为框架,结合地物光谱特征及空间特征,建立以4种归一化指数(归一化三波段指数normalized difference three bands index,NDTBI;归一化建筑指数normalized difference building index,NDBI;改进的归一化水体指数modified normalized difference water index,MNDWI;归一化植被指数normalized difference vegetation index,NDVI)、支持向量机(support vector machine,SVM)分类结果和河流缓冲区作为特征节点的决策树分类器,对贵州省毕节市城乡交接带建设用地专题信息进行提取。NDTBI是该文新构建的指数,取名为归一化三波段指数,目的是为了弥补归一化建筑指数NDBI的不足;支持向量机分类结果的使用在多指数法的基础上提高了地物的可分离性;以构建河流缓冲区的方式加入的地物空间信息,进一步提高了信息提取的精确性。由于决策树特征节点的构建过程是利用先验知识来优化特征值和提高精度的过程,克服了利用单一指数法、多指数法及单独使用模式识别法中出现的问题,精度评价结果显示总体精度达到了97.52%。为了验证方法的推广性,采用毕节市七星关区中心城区遥感影像数据该方法进行验证,精度评价结果显示总体精度达到98.03%。
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| 关 键 词： | 遥感 土地利用 决策树 土地覆盖 建设用地 Landsat-8 |
| 收稿时间： | 2014/2/24 0:00:00 |
| 修稿时间： | 5/4/2014 12:00:00 AM |
Extraction of information on construction land based on multi-feature decision tree classification |
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| Rao Ping,Wang Jianli and Wang Yong.Extraction of information on construction land based on multi-feature decision tree classification[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(12):233-240. |
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| Authors: | Rao Ping Wang Jianli and Wang Yong |
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| Institution: | 1. School of Geography Sciences, Southwest University, Chongqing 400715, China; 2. School of Ecological Engineering, Bijie University, Bijie 551700, China;;1. School of Geography Sciences, Southwest University, Chongqing 400715, China;;1. School of Geography Sciences, Southwest University, Chongqing 400715, China; |
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| Abstract: | Abstract: Spatial distribution status of construction land is closely related to the regional economic and social development. Therefore, timely monitoring and delivery of data on the dynamics of construction land are far-reaching for policy and decision making processes. Classifying land-use/land-cover and analyzing changes are among the most common applications of remote sensing. One of the most basic and difficult classification tasks is to distinguish the construction land from other land surfaces. Landsat imagery is one of the most widely used sources of data in remote sensing of construction land. Several techniques of construction land extraction using Landsat data are described in some literatures, but their applications are constrained by low accuracy in various situations, and usually using the technique of single index or multi-index. The purpose of this study was to devise a method to improve the accuracy of construction land extraction in the presence of various kinds of environmental noise. Thus we introduce a multi-features decision tree (DT) classification model for improving classification accuracy in the areas that including bare land, shadow and some streams, in which the other classification methods often fail to classify correctly. The model integrates four spectral indexes, the pattern recognition technique and spatial algorithms. The four spectral indexes are the normalized difference three bands index (NDTBI), the normalized difference building index (NDBI), the modified normalized difference water index (MNDWI) and the normalized difference vegetation index (NDVI) respectively. The pattern recognition technique is referred to support vector machine (SVM). And the spatial algorithm is to create buffer zone.The test site was deliberately selected so that it consists of complex surface features, such as bare land, hill shade, and some small streams that are liable to be mixed up with construction land on the Landsat imagery. For that reason, Landsat-8 OLI images (path/row 128/41) were selected in sight of its perfect performance in quality. All the Landsat images used are of product type L1T and were geometrically corrected and converted to top-of-atmosphere reflectance consequently. The subset image of one transition zone between urban and rural region in Bijie city of Guizhou province was selected as the test site, the area of which is 144 km2. Besides, the urban center of Qixingguan district of Bijie city was selected to produce thematic map of construction land for application inspection of classification robustness.Building decision tree (DT) nodes is the key of the methodology. Firstly, a new index called NDTBI was developed to be combined with NDBI to identify all the construction land from background. NDTBI and NDBI were separately arranged into the first and second node of DT. However, after executing the DT of the two nodes NDTBI and NDBI, noise such as bare land, shade, water existed together with all construction land at the same time. Secondly, MNDWI and NDVI were added into DT nodes in order to separate water and shadow from construction land. After executing the DT of the above four nodes, the bare land and streams, which share the same spectral feature with construction land, were the only objects mixed up with construction land. Therefore, the SVM classification, in the light of the optimal performance in land-cover classification, provides the opportunity to suppress the bare land noise. Meanwhile, the spatial algorithm was used to separate the small streams from construction land. To execute SVM classification, the training samples were selected by using principal components (PC) transformation of the multi-spectral images. The spatial algorithm is to create buffer zone for the small stream using vectors from visual interpretation. As a result, a segmentation binary image was obtained and added into DT. Two principles were proposed for the buffer zone creating: on the premise that construction land could not be included in the buffer zone, when some channel segment of stream is closely adjacent to the construction land, the width and shape of buffer zone should be same as that of river.To assess the accuracy, the high resolution fusion image (15 m) was taken as the reference dataset, a total of 564 sampling pixels were randomly drawn from the strata (i.e., construction land and non-construction land), 277 pixels from construction land and 287 from non-construction land. The accuracy assessment for test site reached 97.52%. Besides, for application inspection of the classification robustness, the Landsat-8 multispectral images of the urban center of Qixingguan district was used to produce the thematic map of construction land, which proved to be perfect with the overall accuracy 98.03%.The method developed in the paper is easy for understanding and operating, widely applicable, and capable of separating the disturbing noise from construction land. Furthermore, the model dramatically improved the accuracy of extracting construction land from the moderate resolution images. And the validation results suggest the widely applicable in western region of China, especially for the area with land-use/land-cover diversity. |
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| Keywords: | remote sensing land use decision trees land cover construction land Landsat-8 |
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