中文摘要：

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。

英文摘要：

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, acc