陈一祥, 秦昆, 胡忠文, 曾诚. 一种高分影像建筑区分块表示与合并提取方法[J]. 武汉大学学报 ( 信息科学版), 2019, 44(6): 908-916. DOI: 10.13203/j.whugis20170293
引用本文: 陈一祥, 秦昆, 胡忠文, 曾诚. 一种高分影像建筑区分块表示与合并提取方法[J]. 武汉大学学报 ( 信息科学版), 2019, 44(6): 908-916. DOI: 10.13203/j.whugis20170293
CHEN Yixiang, QIN Kun, HU Zhongwen, ZENG Cheng. Built-Up Area Extraction Based on Patch Representation and Merging for High-Resolution Satellite Images[J]. Geomatics and Information Science of Wuhan University, 2019, 44(6): 908-916. DOI: 10.13203/j.whugis20170293
Citation: CHEN Yixiang, QIN Kun, HU Zhongwen, ZENG Cheng. Built-Up Area Extraction Based on Patch Representation and Merging for High-Resolution Satellite Images[J]. Geomatics and Information Science of Wuhan University, 2019, 44(6): 908-916. DOI: 10.13203/j.whugis20170293

一种高分影像建筑区分块表示与合并提取方法

Built-Up Area Extraction Based on Patch Representation and Merging for High-Resolution Satellite Images

  • 摘要: 建筑区是一种重要的人工地理要素,利用高分辨率卫星影像可以在更精细的尺度上获取建筑区信息。针对建筑区这类结构复杂、面积相对较大的地物类,提出一种分块表示与合并提取方法。首先,通过角点上下文约束来划分图像,并将获得的图像块作为影像处理的基本单元;然后,利用空间变异函数来建模每个图像块并提取特征描述参数,进一步通过主成分变换实现建筑区图像块的结构特征表示;最后,根据图像块空间结构特征的相似性实现建筑区的判别。实验结果表明,该方法能够有效实现高分影像建筑区的提取,并且对不同分辨率的高分影像表现出良好的适应性。

     

    Abstract: Built-up areas, which refer to the areas covered by buildings, are important man-made geographical objects, especially in an urban environment. With the increasing availability of high-resolution satellite images, built-up area information can be obtained at a much finer scale. However, the increased spatial resolution makes the built-up areas spectrally more heterogeneous and structurally more complex, which poses a big challenge to the automatic detection of built-up areas. In this paper, a novel built-up area extraction method is proposed based on patch representation and merging algorithm for high-resolution satellite images. First, with the corner context constraints, the image is subdivided into small patches, which are regarded as the basic units of image processing. Afterward, the spatial variability of the image patch is modeled through spatial semivariogram, and texture and structural features are extracted by well-defined parameters to characterize the curve of semivariogram, and to achieve the integrated representation of multiple features for each image patch through a principle component analysis (PCA). Finally, the built-up patches are classified by the similarity of the spatial structural features and further merged into built-up areas. The experiments are conducted on image data from sensors of ZY-3 and QuickBird, and the results show that the proposed method can effectively extract built-up areas from high-resolution satellite images and show good adaptability as the image resolution changes. By using patch-based representation and merging, it can not only avoid the shortcomings of the traditional pixel-based methods and the image segmentation in the object-oriented method, but also can facilitate the modeling and description of the texture and structural features of built-up areas.

     

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