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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 53-60     DOI: 10.6046/zrzyyg.2021089
Remote sensing image segmentation based on Parzen window density estimation of super-pixels
ZHANG Daming1,2(), ZHANG Xueyong1,2, LI Lu1, LIU Huayong1
1. School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230022, China
2. Key Laboratory of Architectural Acoustic Environment of Anhui Higher Education Institutes, Hefei 230601, China
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Image segmentation is a key step in the analysis of high-resolution remote sensing images and plays an important role in improving information extraction accuracy. To improve the performance of traditional pixel-based image segmentation methods, this study proposed a new algorithm based on Parzen window density estimation of super-pixel blocks. The new algorithm includes three main steps, namely super-pixel initial segmentation, feature measurement, and density estimation and re-clustering. In the first step, an image is coarsely divided using the simple linear iterative clustering (SLIC) algorithm, and each super-pixel block is marked as a node in the graph structure of the image. In the second step, the Gabor texture features of each super-pixel block are measured to construct high-dimension feature vectors. Meanwhile, the similarity of the image textures is calculated as the weight of the edge connecting two nodes in the graph. Then, the distance between the two nodes is calculated on the minimum spanning tree (MST) of the graph. In the third step, the calculated distance is used for Parzen window density estimation of each node, and re-clustering of the density values is conducted to obtain the final results. In the experiments, multiple multispectral high-resolution remote sensing images were adopted to verify the algorithm proposed in this study. Using visual discrimination and the quantitative evaluation based on precesion rate and recall rate, the segmentation results of the algorithm proposed in this study were compared with those of other algorithms. The experiments verified that the algorithm proposed in this study is effective.

Keywords multispectral remote sensing image      Parzen window density estimation      super-pixel      minimum spanning tree      image segmentation      region merging     
ZTFLH:  TP391  
Issue Date: 14 March 2022
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Daming ZHANG
Xueyong ZHANG
Huayong LIU
Cite this article:   
Daming ZHANG,Xueyong ZHANG,Lu LI, et al. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
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Fig.1  Cluster based on Parzen windows density estimation on minimum spanning tree
Fig.2  Flow chart of the proposed multispectral remote sensing image segmentation algorithm
Fig.3  Original image of experiment 1
参数 S
5 20 50
Tab.1  Segmentation results of experiment 1
参数S 5 20 50
平均计算时间 58.487 6 19.783 4 14.347 2
Tab.2  Computing time versus parameter S in experiment 1(s)
h S=5 S=20 S=50
Precision Recall Precision Recall Precision Recall
0.5 0.913 4 0.927 2 0.914 6 0.958 6 0.793 4 0.828 3
5 0.917 2 0.957 3 0.916 2 0.959 5 0.814 1 0.841 3
20 0.904 6 0.921 7 0.920 7 0.938 6 0.819 2 0.839 6
50 0.831 5 0.874 6 0.742 8 0.789 1 0.732 5 0.749 3
Tab.3  Segmentation results evaluation of experiment 1
Fig.4  Segmentation results of experiment 2
Fig.5  Segmentation results of experiment 3
指标 FNEA 本文方法
50 150 S=5 S=20 S=30
Precision 0.782 9 0.798 9 0.783 6 0.879 8 0.884 2
Recall 0.879 6 0.882 7 0.870 7 0.936 8 0.937 1
Tab.4  Segmentation results evaluation of experiment 2

FNEA 本文方法
150 200 S=5 S=20 S-30
Precision 0.841 1 0.856 3 0.897 2 0.903 7 0.912 7
Recall 0.884 3 0.892 7 0.912 6 0.921 9 0.930 5
Tab.5  Segmentation results evaluation of experiment 3
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