A Novel Texture-Preceded Segmentation Algorithm for High-Resolution Imagery
A Novel Texture-Preceded Segmentation Algorithm for High-Resolution Imagery
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DOI：
10.1109/tgrs.2010.2041462
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发表时间：
2010-03
影响因子：
8.2
通讯作者：
Nan Li;H. Huo;T. Fang
中科院分区：
文献类型：
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作者：
Nan Li;H. Huo;T. Fang
图像分割是面向对象的遥感图像分析的关键。针对高分辨率遥感图像，提出了一种新的基于纹理的图像分割算法，该算法首先将纹理聚类作为后续分割的松散约束。该算法基于区域邻接图和最近邻图两种图模型，以全局最优为依据，实现快速节点合并。在这里，一个组合的距离，由纹理，光谱和形状特征，建立衡量节点之间的相似性，并给出相同的语义描述的纹理对象。然后，将组合距离应用于图模型，通过快速合并迭代得到最终分割结果。在合并过程中，最优序列合并与纹理聚类相互作用，以细化纹理区域的真实的边缘。该算法不仅能很好地融合具有光谱变化性的均匀纹理段，而且能很好地检测出真实的目标边界。在高分辨率遥感影像上的实验表明，在相同的分割数下，该算法比单纯光谱特征的分割精度提高10%~ 20%。
Image segmentation is crucial to object-oriented remote sensing imagery analysis. In this paper, a novel texture-preceded segmentation algorithm is proposed for high-resolution remote sensing imagery, in which texture clustering is first carried out as a loose constraint for later segmentation. The algorithm is based on the graph models of region adjacency graph and nearest neighbor graph, which can achieve fast node merging, depending on the global optimum. Here, a combined distance, composed of texture, spectral, and shape features, is established to measure the similarity between nodes and gives the same semantic descriptions for the texture objects. Then, the combined distance is applied to graph models, and the final segmentation result can be obtained iteratively by fast merging. During the merging process, optimal sequence merging interacts with texture clustering to refine the real edges of a texture region. This algorithm cannot only merge the homogeneous texture segments with spectral variability easily but can also detect the real object boundaries well. The experiments on high-resolution imagery show that, in terms of the same number of segments, the proposed algorithm can improve segmentation accuracy by 10%-20% compared to the results obtained by pure spectral features with Definiens Developer software.