Automatic segmentation of high-resolution satellite imagery by integrating texture, intensity, and color features
Automatic segmentation of high-resolution satellite imagery by integrating texture, intensity, and color features
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DOI：
10.14358/pers.71.12.1399
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发表时间：
2005-12-01
影响因子：
1.3
通讯作者：
Prenzel, B
中科院分区：
文献类型：
--
作者：
Hu, XY;Tao, CV;Prenzel, B
高空间分辨率卫星图像已成为地理空间应用的一个重要信息来源。高分辨率卫星图像的自动分割有助于获得更及时、更准确的信息。在本文中，我们开发了一种方法和算法框架，自动分割图像到不同的区域对应的纹理，强度和颜色的各种功能。该方法的核心原理是，从三个特征通道的信息被自适应估计和集成到一个分裂合并加像素细化框架。在分割合并和细化过程中，通过比较子区域不同特征之间的相似性来实现分割。相似性度量基于特征分布。在没有图像内容的先验知识的情况下，图像可以被分割成不同的区域，这些区域通常对应于不同的土地使用或其他物体。实验结果表明，该方法在正确性和适应性方面比使用单个特征或多个特征，但每个特征的权重不变。该方法可以潜在地应用于广泛的图像分割上下文。
High spatial resolution satellite imagery has become an important source of information for geospatial applications. Automatic segmentation of high-resolution satellite imagery is useful for obtaining more timely and accurate information. In this paper, we develop a method and algorithmic framework for automatically segmenting imagery into different regions corresponding to various features of texture, intensity and color. The central rationale of the method is that information from the three feature channels are adoptively estimated and integrated into a split-merge plus pixel-wise refinement framework. In the procedure for split-merge and refinement, segmentation is realized by comparing similarities between different features of sub-regions. The similarity measure is based on feature distributions. Without a priori knowledge of image content, the image can be segmented into different regions that frequently correspond to different land-use or other objects. Experimental results indicate that the method Performs much better in terms of correctness and adaptation than using single feature or multiple features, but with constant weight for each feature. The method can potentially be applied within a broad range of image segmentation contexts.