High Resolution Remote Sensing Image Classification Combining with Mean-Shift Segmentation and Fully Convolution Neural Network

Fig. 6. Classification results of different methods. (a) Original image; (b) segmentation result of mean-shift ①;(c) segmentation result of mean-shift ②; (d) segmentation result of mean-shift ③; (e) true classification image;(f) classification result of SVM ; (g) classification result of ANN; (h) classification result of FCN-16; (i) classification result of FCN-8; (j) classification result of proposed FCN; (k) classification result of proposed FCN adding segmentation result of mean-shift ①; (l) classifi

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Fig. 7. Marked images of some details. (a) True classification image; (b) classification result of FCN-16; (c) classification result of proposed FCN adding segmentation result of mean-shift ②

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Table 1. Confusion matrice and overall accuracy of SVM classification method%
View table
Table 1. Confusion matrice and overall accuracy of SVM classification method%
Type ofground B F W R S G
B 81.7 8.1 2.1 45.2 64.1 45.8
F 3.8 70.0 15.1 8.8 7.2 6.9
W 0.5 0.4 79.6 0 0.1 0
R 11.6 14.4 2.5 43.6 13.3 4.5
S 0.7 0.1 0.1 0.2 6.7 0.8
G 1.7 7.1 0.7 2.1 8.6 42.0
OA 66.08

Table 2. Confusion matrice and overall accuracy of ANN classification method%
View table
Table 2. Confusion matrice and overall accuracy of ANN classification method%
Type ofground B F W R S G
B 82.0 11.2 2.5 51.1 72.5 53.0
F 4.4 68.3 12.0 7.4 8.1 32.5
W 0.4 3.7 83.4 0.6 1.9 2.8
R 13.2 16.8 2.2 40.9 17.5 11.7
S 0 0 0 0 0 0
G 0 0 0 0 0 0
OA 64.79

Table 3. Confusion matrice and overall accuracy of FCN-16 classification method%
View table
Table 3. Confusion matrice and overall accuracy of FCN-16 classification method%
Type ofground B F W R S G
B 86.3 2.6 0 18.86 34.53 0.38
F 4.1 82.7 5.84 20.93 28.52 51.52
W 1.7 1.7 92.56 3.81 2.88 11.69
R 2.7 5.6 0.14 34.56 1.70 0.14
S 3.8 1.9 0.30 1.32 19.80 13.37
G 1.4 5.6 1.16 20.51 12.57 22.91
OA 71.6

Table 4. Confusion matrice and overall accuracy of FCN-8 classification method%
View table
Table 4. Confusion matrice and overall accuracy of FCN-8 classification method%
Type ofground B F W R S G
B 83.54 3.68 0.14 21.24 35.77 8.49
F 8.13 89.59 8.93 38.10 28.27 36.16
W 1.91 0.57 89.92 2.16 2.90 2.18
R 2.46 3.25 0.46 22.96 5.54 2.40
S 1.06 0.19 0.01 1.67 9.77 1.61
G 2.90 2.72 0.54 13.87 17.75 49.16
OA 68.8

Table 5. Confusion matrice and overall accuracy of proposed FCN classification method%
View table
Table 5. Confusion matrice and overall accuracy of proposed FCN classification method%
Type ofground B F W R S G
B 86.2 0.83 0 10.61 14.3 1.8
F 1.97 82.41 2.34 11.15 19.4 26.2
W 0.23 0.30 96.77 3.96 2.7 0
R 4.59 13.39 0.55 69.51 10.4 1.1
S 6.36 1.81 0.32 3.36 52.7 4.7
G 0.62 1.26 0.02 1.42 0.4 66.3
OA 80.90

Table 6. Confusion matrice and overall accuracy of proposed FCN adding segmentation result of mean-shift ①%
View table
Table 6. Confusion matrice and overall accuracy of proposed FCN adding segmentation result of mean-shift ①%
Type ofground B F W R S G
B 86.7 0.8 0 14.1 18.5 7.4
F 1.2 84.7 2.6 9.9 17.3 10.0
W 0 0.6 96.2 2.9 1.7 0
R 4.5 11.5 1.0 68.9 9.3 4.5
S 5.3 2.0 0.2 4.1 53.1 9.6
G 0.3 0.4 0 0.1 0.1 68.6
OA 82.1

Table 7. Confusion matrice and overall accuracy of proposed FCN adding segmentation result of mean-shift ②%
View table
Table 7. Confusion matrice and overall accuracy of proposed FCN adding segmentation result of mean-shift ②%
Type ofground B F W R S G
B 91.0 1.8 0 17.9 16.5 8.9
F 1.0 82.6 2.9 8.7 14.3 14.3
W 0 0.8 95.0 2.1 1.6 0
R 5.4 13.1 1.6 69.5 9.0 0.3
S 2.1 1.7 0.6 1.8 58.5 0
G 0.5 0.1 0 0 0.1 76.5
OA 83.5

Table 8. Confusion matrice and overall accuracy of proposedFCN adding segmentation result of mean-shift ③%
View table
Table 8. Confusion matrice and overall accuracy of proposedFCN adding segmentation result of mean-shift ③%
Type ofground B F W R S G
B 85.5 1.0 0 18.2 18.9 12.8
F 0.9 79.1 2.0 8.2 12.9 0
W 0 0.6 94.5 2.5 1.2 0
R 8.7 17.8 3.2 68.0 15.3 5.7
S 3.9 1.3 0.3 2.3 51.1 0
G 1.0 0.1 0 0.8 0.7 81.5
OA 79.4

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Xu Fang, Guanghui Wang, Huachao Yang, Huijie Liu, Libo Yan. High Resolution Remote Sensing Image Classification Combining with Mean-Shift Segmentation and Fully Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(2): 022802

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Paper Information

Category: Remote Sensing and Sensors

Received: Aug. 2, 2017

Accepted: --

Published Online: Sep. 10, 2018

The Author Email: Xu Fang (fangxu622@126.com)

DOI:10.3788/LOP55.022802

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Confusion matrice and overall accuracy of SVM classification method%

Table 1. Confusion matrice and overall accuracy of SVM classification method%

Table 2. Confusion matrice and overall accuracy of ANN classification method%

Table 2. Confusion matrice and overall accuracy of ANN classification method%

Table 3. Confusion matrice and overall accuracy of FCN-16 classification method%

Table 3. Confusion matrice and overall accuracy of FCN-16 classification method%

Table 4. Confusion matrice and overall accuracy of FCN-8 classification method%

Table 4. Confusion matrice and overall accuracy of FCN-8 classification method%

Table 5. Confusion matrice and overall accuracy of proposed FCN classification method%

Table 5. Confusion matrice and overall accuracy of proposed FCN classification method%

Table 6. Confusion matrice and overall accuracy of proposed FCN adding segmentation result of mean-shift ①%

Table 6. Confusion matrice and overall accuracy of proposed FCN adding segmentation result of mean-shift ①%

Table 7. Confusion matrice and overall accuracy of proposed FCN adding segmentation result of mean-shift ②%

Table 7. Confusion matrice and overall accuracy of proposed FCN adding segmentation result of mean-shift ②%

Table 8. Confusion matrice and overall accuracy of proposedFCN adding segmentation result of mean-shift ③%

Table 8. Confusion matrice and overall accuracy of proposedFCN adding segmentation result of mean-shift ③%