随着遥感技术的迅速发展，遥感影像向高空间、高光谱、高时间分辨率方向发展，使得影像上地物的颜色、纹理、形状等特征信息更加突出。因此，利用高分辨率遥感影像对地物进行精确识别成为可能。但由于遥感影像的复杂性，计算机自动解译无法满足精度要求，在实际应用中多依靠人工目视解译，然而这种方式需要耗费大量人力物力，且效率较低，面对大量的遥感数据已经显得严重不足。基于以上考虑，交互式提取方法是目前一种可行的代替方案。该方法充分发挥人的识别能力与计算机的处理能力，不仅保证了解译精度，同时也提高了解译效率。 本文以交互式提取高分辨率遥感影像上典型地物为研究目标，分别对影像上直角建筑物和自然地物的交互式提取方法进行了研究，研究内容如下： （1）探索直角建筑物的交互式提取方法。针对直角建筑物具有星形形状这一特点，提出了一种基于多星形形状约束图割的交互式提取方法。算法首先在人工建筑物上画一条线，自动得到包含目标建筑物的影像图块，然后对该影像块进行预处理和过分割，再利用多星形先验图割方法得到建筑物图斑，最后结合线段方向直方图和角点拟合分组对建筑物图斑规则化，从而获取建筑物准确轮廓。实验在两幅不同地区和不同空间分辨率的高分航空影像上验证了本文方法的准确性、高效性和稳定性。 （2）探索面状自然地物的交互式提取方法。针对水域、林地、梯田、裸露地等含有丰富颜色与纹理信息的面状自然地物，提出了一种基于全连接条件随机场的交互式提取方法。算法首先以对影像过分割为基础，提取每个对象的颜色与纹理特征，然后通过人工交互标记前景样本估计前景背景模型，再建立全连接条件随机场模型描述影像的全局信息，最后利用高维高斯滤波支持下的均值场估计方法实现模型推断，从而获取目标轮廓。通过对一幅高分辨率遥感影像上多种地物进行提取实验，实验结果证明了本文方法的有效性。

With the rapid development of acquisition technology of very high resolution (VHR) remote sensing image, the development of remote sensing images in the direction of high space, hyperspectral, and high temporal resolution makes the features such as color, texture, and shape of the image more prominent. Therefore, it is possible to accurately extracting objects using VHR remote sensing images. However, due to the complexity of remote sensing images, automatic interpretation of computer can not meet the accuracy requirements. In practical applications, it mostly depends on visual interpretation of human, which requires a lot of manpower and material resources and is inefficient. With the large amount of data from remote sensing, artificial visual methods have become seriously inadequate. Based on the above considerations, the interactive extraction method as an alternative method, combines full people's recognition ability and computer's processing ability, which not only guarantees the understanding of translation accuracy, but also improves the understanding of translation efficiency. This paper carries out the research on the interactive extraction method of right-angle buildings and natural objects from VHR remote sensing images, respectively. The research contents are as follows: (1) For the right-angle buildings with a star shape, this paper propose a method of interactively extracting right-angle buildings. Firstly, the image block containing the target building is obtained by manual interaction. Next, the image block is preprocessed by bilateral filtering. Then the graph cuts with the star shape constraint is used to obtain the building objects. Finally, building object is regularized into real regular shape through corner detection and linear fitting. The experiments performed on two different region and spatial resolution aerial imageries demonstrate the stability and accuracy of the proposed method. (2) For the water, woodland, terraced fields, and bare ground, called natural objects, with abundant color and texture information, this paper proposes a method of interactively extracting nature objects based on fully connected conditional random fields. Firstly, the input image is segmented to obtain superpixels with similar characteristics. Then the foreground samples are marked by human interaction. Next, the color and texture features of each superpixel are extracted. Then a fully-connected conditional random field model is established to combine the user interaction markers. Finally, using the mean-field estimation supported by the high-dimensional Gaussian filtering to achieve model inference, the target contour is obtained. By extracting a variety of ground features from a high-resolution remote sensing image, the experimental results prove the effectiveness of the proposed method.