基于遥感影像的建筑物自动提取方法容易受混合像元影响,目标提取精度不高。亚像元定位可以提取亚像元尺度地物分布信息,减轻混合像元对目标提取结果造成的影响。传统亚像元定位模型采用各向同性邻域描述地物的空间相关性,并没有考虑地物特有的形状信息,难以满足建筑物提取的需要。在考虑建筑物光谱特征的基础上,建立了平行与垂直于目标建筑物主方向的各向异性邻域,并采用基于各向异性Markov随机场的亚像元定位模型进行了亚像元尺度的建筑物提取。基于QuickBird多光谱数据与AVIRIS高光谱数据的实验结果表明,该模型提取的建筑物不仅具有更高的空间分辨率,而且能够较好地保持建筑物边缘与角点的形状信息,是一种有效的亚像元尺度建筑物提取方法。

Automatic building extraction from remotely sensed images is affected by the mixed pixel problem that lowers the accuracy of the extracted buildings. Sub-pixel mapping is a procedure to predict the land cover maps at the sub-pixel scale, and hence reduceing the influence of the mixed pixel problem. However, the sub-pixel mapping models adopt isotropic neighborhood to calculate land cover spatial dependence for simplicity, instead of using prior spatial information of buildings, making the shapes of the resultant building inaccurate. In this paper, a novel anisotropic Markov random field based sub-pixel mapping (AMSPM)approach, which manages the spectral information of the remotely sensed image and the a priori information of buildings simultaneously, is used for extracting the buildings at the sub-pixel scale. In the proposed model, an anisotropic neighborhood that only encourages the land cover dependence that both, parallel and perpendicular to the principal axis orientation of the target building, is adoed as the prior information of a building. A QuickBird multi-spectral image and an Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS)hyperspectral image are applied and our results shows the propose method can not only enhance the spatial resolution of the extracted buildings, but also preserves the edge and the corner shape of the extracted buildings. The proposed model is effective for extracting buildings at the sub-pixel scale.