A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation
A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation
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DOI：
10.1016/j.isprsjprs.2014.04.008
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发表时间：
2014-08
影响因子：
12.7
通讯作者：
Jian Yang;Peijun Li;Yuhong He
中科院分区：
文献类型：
--
作者：
Jian Yang;Peijun Li;Yuhong He
图像分割是基于目标的高分辨率图像分析的关键步骤之一。在图像分割中，选择合适的尺度参数是一项特别重要的任务。本研究提出了一种无监督多波段图像分割过程中尺度参数选择的方法，该方法利用光谱角来衡量图像片段的光谱均匀性。随着尺度参数的增大，图像片段的光谱均匀性逐渐降低，直至与真实世界的目标相匹配。利用光谱均匀性指标确定多个合适的尺度参数。通过定性的视觉解释和定量的差异测量，将该方法的性能与基于单波段的方法进行了比较。这两种方法都用于分割两幅图像：QuickBird场景中的中国北京市区和Woldview-2场景中的日本柏和郊区。本文提出的基于多波段分割尺度参数选择方法优于基于单波段分割尺度参数选择方法，对不同城市景观中不同土地覆盖对象的识别效果更好。
Image segmentation is one of key steps in object based image analysis of very high resolution images. Selecting the appropriate scale parameter becomes a particularly important task in image segmentation. In this study, an unsupervised multi-band approach is proposed for scale parameter selection in the multi-scale image segmentation process, which uses spectral angle to measure the spectral homogeneity of segments. With the increasing scale parameter, spectral homogeneity of segments decreases until they match the objects in the real world. The index of spectral homogeneity is thus used to determine multiple appropriate scale parameters. The performance of the proposed method is compared to a single-band based method through qualitative visual interpretation and quantitative discrepancy measures. Both methods are applied for segmenting two images: a QuickBird scene of an urban area within Beijing, China and a Woldview-2 scene of a suburban area in Kashiwa, Japan. The proposed multi-band based segmentation scale parameter selection method outperforms the single-band based method with the better recognition for diverse land cover objects in different urban landscapes.