Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1410016(2021)
Fuzzy Clustering Remote Sensing Image Water Segmentation Algorithm Combined with Gravity Model
Figures & Tables(12)
![Segmentation results of remote sensing image 1 by different algorithms. (a) Original image; (b) FCM segmentation result; (c) FLICM segmentation result; (d) KNLASC-FCM[9] segmentation result; (e) IIFCM[17] segmentation result; (f) NDFCM[7] segmentation result; (g) SNLS_IFCM[22] segmentation result; (h) segmentation result of our algorithm](/Images/icon/loading.gif){kind=icon}
Fig. 2. Segmentation results of remote sensing image 1 by different algorithms. (a) Original image; (b) FCM segmentation result; (c) FLICM segmentation result; (d) KNLASC-FCM[9] segmentation result; (e) IIFCM[17] segmentation result; (f) NDFCM[7] segmentation result; (g) SNLS_IFCM[22] segmentation result; (h) segmentation result of our algorithm
Fig. 3. Segmentation results of remote sensing image 2 by different algorithms. (a) Original image; (b) FCM segmentation result; (c) FLICM segmentation result; (d) KNLASC-FCM[9] segmentation result; (e) IIFCM[17] segmentation result; (f) NDFCM[7] segmentation result; (g) SNLS_IFCM[22] segmentation result; (h) segmentation result of our algorithm
Fig. 4. Segmentation results of remote sensing image 3 by different algorithms. (a) Original image; (b) FCM segmentation result; (c) FLICM segmentation result; (d) KNLASC-FCM[9] segmentation result; (e) IIFCM[17] segmentation result; (f) NDFCM[7] segmentation result; (g) SNLS_IFCM[22] segmentation result; (h) segmentation result of our algorithm
Fig. 5. Segmentation results of remote sensing image 4 by different algorithms. (A) Original image; (b) FCM segmentation result; (c) FLICM segmentation result; (d) KNLASC-FCM[9] segmentation result; (e) IIFCM[17] segmentation result; (f) NDFCM[7] segmentation result; (g) SNLS_IFCM[22] segmentation result; (h) segmentation result of our algorithm
Fig. 6. Segmentation results of remote sensing image 5 by different algorithms. (a) Original image; (b) FCM segmentation result; (c) FLICM segmentation result; (d) KNLASC-FCM[9] segmentation result; (e) IIFCM[17] segmentation result; (f) NDFCM[7] segmentation result; (g) SNLS_IFCM[22] segmentation result; (h) segmentation result of our algorithm
Fig. 7. Segmentation results of remote sensing image 6 by different algorithms. (a) Original image; (b) FCM segmentation result; (c) FLICM segmentation result; (d) KNLASC-FCM[9] segmentation result; (e) IIFCM[17] segmentation result; (f) NDFCM[7] segmentation result; (g) SNLS_IFCM[22] segmentation result; (h) segmentation result of our algorithm
Fig. 8. Reference images of water segmentation. (a) reference segmentation image of image 1; (b) reference segmentation image of image 2; (c) reference segmentation image of image 3; (d) reference segmentation image of image 4; (e) reference segmentation image of image 5; (f) reference segmentation image of image 6
Table 1. Accuracy of segmentation results unit: %
View tableTable 1. Accuracy of segmentation results unit: %
Method Image 1 Image 2 Image 3 Image 4 Image 5 Image 6 FCM 87.3 79.7 75.4 86.4 82.7 84.3 FLICM 91.2 86.2 80.3 90.6 87.4 93.3 KNLASC-FCM[ 9 ]90.7 83.5 81.7 89.6 86.6 84.9 IIFCM[ 17 ]94.8 91.3 88.6 88.7 88.5 96.8 NDFCM[ 7 ]91.7 83.9 72.8 90.3 88.1 91.2 SNLS_IFCM[ 22 ]95.6 84.8 76.3 91.5 89.3 91.6 Proposed 96.3 94.8 89.2 93.4 90.8 97.1
Table 2. False alarm rate of segmentation results unit: %
View tableTable 2. False alarm rate of segmentation results unit: %
Method Image 1 Image 2 Image 3 Image 4 Image 5 Image 6 FCM 27.3 37.9 35.7 26.8 24.3 18.2 FLICM 19.7 28.3 33.6 19.7 15.2 10.1 KNLASC-FCM[ 9 ]25.4 34.2 31.4 14.9 14.6 19.3 IIFCM[ 17 ]18.6 14.7 17.6 15.6 9.4 4.3 NDFCM[ 7 ]6.4 33.9 39.1 18.5 10.9 9.8 SNLS_IFCM[ 22 ]7.3 32.6 34.2 11.7 8.2 8.6 Proposed 5.2 7.9 13.8 9.3 7.7 3.7
Table 3. Mean intersection of union of segmentation results
View tableTable 3. Mean intersection of union of segmentation results
Method Image 1 Image 2 Image 3 Image 4 Image 5 Image 6 FCM 0.81 0.74 0.67 0.71 0.73 0.81 FLICM 0.87 0.82 0.73 0.84 0.81 0.88 KNLASC-FCM[ 9 ]0.83 0.76 0.71 0.79 0.77 0.82 IIFCM[ 17 ]0.91 0.88 0.82 0.77 0.84 0.93 NDFCM[ 7 ]0.89 0.79 0.64 0.82 0.82 0.89 SNLS_IFCM[ 22 ]0.91 0.81 0.72 0.83 0.85 0.91 Proposed 0.94 0.92 0.86 0.87 0.89 0.94
Table 4. Time complexity of different methods
View tableTable 4. Time complexity of different methods
Method Number of computational steps Time complexity FCM N×c×T O(n3) FLICM N×c×S×T O(n4) KNLASC-FCM[ 9 ]N×c×S×T O(n4) IIFCM[ 17 ]N+N×c×S×T O(n4) NDFCM[ 7 ]N×c×S×s×T O(n5) SNLS_IFCM[ 22 ]N×S+N×c×S×s×T O(n5) Proposed N×S+N×c×S×T O(n4)
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Qi Zhang, Guiqin Yang, Xiaopeng Wang. Fuzzy Clustering Remote Sensing Image Water Segmentation Algorithm Combined with Gravity Model[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1410016
Paper Information
Category: Image Processing
Received: Oct. 19, 2020
Accepted: Nov. 18, 2020
Published Online: Jun. 30, 2021
The Author Email: Guiqin Yang (yangguiqin@mail.lzjtu.cn)
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