Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree
Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree
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DOI：
10.3390/rs12050783
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Wenjie Lin;Yu Li
中科院分区：
文献类型：
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作者：
Wenjie Lin;Yu Li
高分辨率图像以更精细的空间尺度提供了复杂的、空间的、海量的地球表面信息，这给遥感分割方法带来了新的挑战。针对这些挑战，寻找更有效的分割模型和并行处理方法对于提高大尺度高分辨率图像的分割精度和处理效率至关重要。为此，本研究提出了一种结合最小生成树（MST）模型的基于区域的并行分割方法。首先，通过规则细分将图像分解为若干块。在多核并行处理模式下，采用最小异构规则（MHR）划分技术获得相应的均匀区域，并采用并行块合并方法获得初始分割结果。在此基础上，提出了一种基于主从并行模式的区域模糊c均值（FCM）方法，以实现快速最优分割。在高分辨率图像上对该分割方法进行了测试。定性评价、定量评价和并行分析的结果验证了所提方法的可行性和有效性。
With finer spatial scale, high-resolution images provide complex, spatial, and massive information on the earth’s surface, which brings new challenges to remote sensing segmentation methods. In view of these challenges, finding a more effective segmentation model and parallel processing method is crucial to improve the segmentation accuracy and process efficiency of large-scale high-resolution images. To this end, this study proposed a minimum spanning tree (MST) model integrated into a regional-based parallel segmentation method. First, an image was decomposed into several blocks by regular tessellation. The corresponding homogeneous regions were obtained using the minimum heterogeneity rule (MHR) partitioning technique in a multicore parallel processing mode, and the initial segmentation results were obtained by the parallel block merging method. On this basis, a regionalized fuzzy c-means (FCM) method based on master-slave parallel mode was proposed to achieve fast and optimal segmentation. The proposed segmentation approach was tested on high-resolution images. The results from the qualitative assessment, quantitative evaluation, and parallel analysis verified the feasibility and validity of the proposed method.