Super-Pixel Segmentation of Remote Sensing Image Based on Improved Simple Linear Iterative Clustering Algorithm

Xinlei Ren; Yangping Wang

doi:10.3788/LOP57.222801

Laser & Optoelectronics Progress, Volume. 57, Issue 22, 222801(2020)

Super-Pixel Segmentation of Remote Sensing Image Based on Improved Simple Linear Iterative Clustering Algorithm

Xinlei Ren^1、* and Yangping Wang^1,2,3,4

Author Affiliations

¹School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

²National Experimental Teaching Demonstration Center of Computer Science and Technology, Lanzhou Jitotong University, Lanzhou, Gansu 730070, China

³Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing, Lanzhou, Gansu 730070, China;

⁴Gansu Provincial Key Laboratory of System Dynamics and Reliability of Rail Transport Equipment, Lanzhou, Gansu 730070, China

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Figures & Tables(10)

Fig. 1. Search space of K-means and SLIC algorithm. (a) K-means algorithm; (b) SLIC algorithm

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Fig. 2. Super-pixel segmentation results of SLIC algorithm. (a) Original image; (b) K=100; (c) K=300; (d) K=500

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Fig. 3. Initialization process of clustering center. (a) Random selection; (b) computational gradient; (c) clustering center

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Fig. 4. Super-pixel segmentation result obtained by SLIC algorithm. (a) Input image; (b) super-pixel segmentation result; (c) partial enlarged drawing

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Fig. 5. Comparison of segmentation results. (a) SLIC algorithm; (b) improved algorithm

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Fig. 6. Super-pixel segmentation results of different algorithms (K=100). (a) SLIC0 algorithm; (b) Ref.[23]; (c) TurboPixel algorithm; (d) SLIC algorithm; (e) our algorithm

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Fig. 7. Average running time of 5 algorithms

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Fig. 8. Segmentation accuracy of different algorithms. (a) UE; (b) ASA

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Table 1. Running time of 5 algorithms (K=100) unit: s
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Table 1. Running time of 5 algorithms (K=100) unit: s
Algorithm SLIC0 Ref.[23] TurboPixel SLIC Ours
Running time 2.53 1.55 18.05 3.425 1.25

Table 2. Edge fitting capabilities of different algorithms (K=100)
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Table 2. Edge fitting capabilities of different algorithms (K=100)
Algorithm SLIC0 Ref.[23] TurboPixel SLIC Ours
UE 0.266 0.233 0.358 0.296 0.222
ASA 0.959 0.945 0.935 0.950 0.964

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Xinlei Ren, Yangping Wang. Super-Pixel Segmentation of Remote Sensing Image Based on Improved Simple Linear Iterative Clustering Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(22): 222801

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