Object-based sub-pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery
Object-based sub-pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery
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DOI：
10.1016/j.jag.2012.02.008
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
F. Ling;Xiaodong Li;Fei Xiao;Shiming Fang;Yun Du
中科院分区：
文献类型：
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作者：
F. Ling;Xiaodong Li;Fei Xiao;Shiming Fang;Yun Du
亚像元制图（subpixel mapping， SPM）是一种很有前景的遥感影像亚像元尺度土地覆被类型空间位置预测方法，它将软分类生成的影像作为输入。目前，SPM以相同的策略处理不同土地覆盖类别的所有子像元，最大限度地提高它们的空间依赖性。尽管最大空间依赖关系是描述土地覆盖类别空间格局的一种简单方法，并且已被证明是SPM的有效原理，但它并不能反映现实情况。鉴于空间格局是特定于土地覆盖类别或对象的，在应用SPM时，每个土地覆盖类别或对象都应指定其特定的空间格局描述。本文提出了一种新的基于目标的亚像素映射（OBSPM）方法，用于亚像素尺度的建筑物映射。基于建筑物形状的先验信息（即建筑物边界与主方向平行或垂直），在SPM过程中采用了一种新的各向异性空间依赖模型。提出的OBSPM模型包括三个主要步骤：建筑物分割、建筑物特征提取和建筑物各向异性SPM。用模拟合成图像和实际的AVIRIS图像对该模型进行了评价。结果表明，OBSPM模型比传统的SPM模型获得更精确的建筑物地图，而分数像的精度和遥感影像的空间分辨率是影响OBSPM结果的两个关键因素。此外，将OBSPM模型扩展到更多的土地覆盖类别，以纳入更具体的先验信息，是提高SPM模型对实际情况适用性的一种有希望的方法。
Sub-pixel mapping (SPM) is a promising method to predict the spatial locations of land cover classes at the sub-pixel scale for remotely sensed imagery, using the fraction images generated by soft classification as input. At present, SPM treats all sub-pixels of different land cover classes in the same strategy by maximizing their spatial dependence. Although the maximal spatial dependence is a simple method to describe the spatial pattern of land cover classes and has been proved to be an effective principle for SPM, it does not reflect real-world situations. Given that spatial patterns are land cover class- or object-specific, each land cover class or object should be designated its own specific spatial pattern description when SPM is applied. In this paper, a novel object-based sub-pixel mapping (OBSPM) method was proposed to map buildings at the sub-pixel scale. On the basis of the prior information of the building shape (i.e., the building boundaries are parallel or perpendicular to the main orientation), a novel anisotropic spatial dependence model is adopted in the SPM procedure. The proposed OBSPM model includes three main steps: building segmentation, building feature extraction, and anisotropic SPM of buildings. The proposed model is evaluated with a simulated synthetic image and an actual AVIRIS image. The results show that OBSPM obtains more accurate building maps than do conventional SPM models, and the accuracy of fraction images and the spatial resolutions of remotely sensed images are two crucial factors that influence the OBSPM results. Furthermore, extending the OBSPM model to more land cover classes to incorporate more specific prior information is a promising method in enhancing the applicability of SPM to practical situations.