联合像素与多尺度对象的高分辨率遥感影像谱聚类分割

doi:10.13229/j.cnki.jdxbgxb20180537

Abstract

Abstract:

An spectral clustering segmentation method for high spatial resolution (HSR) remote sensing image based on combination of pixels and multi-scale objects is proposed. The algorithm first focus on building a graph that integrates multi-scale information as well as improve similarity computation method between objects. Then, the similarity matrix is computed on the graph, and normalized cut criterion is used to similarity matrix eigen-decomposition so that the original data is mapped into the low-dimensional subspace. Finally, the clustering algorithm is used to complete the image segmentation after the selected subset of the eigenvectors. In order to prove the effectiveness of the algorithm, we choose high spatial resolution remote sensing images to conduct experiment and compare to the state-of-the-art techniques. The experimental result show that three of four experimental indexes are superior to other algorithms, which proves the effectiveness of this proposed method segmentation method.

Key words: photogrammetry and remote sensing, remote sensing image segmentation, spectral clustering, normalized cut, multi?scale objects

CLC Number:

Jun-jun LI,Jian-nong CAO,Bei-bei CHENG,Juan LIAO,Ying-ying ZHU. High spatial resolution remote sensing imagery segmentation based on combination of pixels and multi⁃scaleobjects using spectral clustering[J].Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 2098-2108.

Figures/Tables 7

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指标	影像1	影像2	影像3	影像4	影像5	影像6	影像7	影像8	影像9	影像10
PRI	0.905 7	0.967 00	0.975 1	0.972 4	0.975 6	0.943 0	0.833 0	0.952 5	0.967 1	0.958 0
GCE	2.340 9	1.564 10	2.153 5	2.238 8	1.976 0	1.926 0	2.269 2	2.267 2	1.473 0	1.825 7
VoI	0.272 3	0.234 90	0.333 0	0.351 1	0.302 4	0.273 2	0.230 3	0.312 1	0.207 7	0.250 4
BDE	6.371 3	2.431 90	5.868 7	2.629 0	3.284 1	4.342 8	4.211 1	4.955 6	2.370 0	3.263 2

指标	影像1	影像2	影像3	影像4	影像5	影像6	影像7	影像8	影像9	影像10
PRI	0.903 4	0.964 4	0.979 5	0.977 7	0.966 2	0.943 6	0.837 7	0.961 5	0.969 5	0.936 8
GCE	2.527 5	1.688 1	2.203 2	2.284 3	1.900 7	1.968 5	2.395 8	2.330 2	1.500 1	2.286 0
VoI	0.325 4	0.239 0	0.357 1	0.380 8	0.305 5	0.312 0	0.296 0	0.352 2	0.214 1	0.354 6
BDE	7.581 51	2.701 3	6.107 2	2.508 1	3.271 2	4.786 3	4.661 5	4.874 3	2.367 3	3.793 9

High spatial resolution remote sensing imagery segmentation based on combination of pixels and multi⁃scaleobjects using spectral clustering

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