激光与光电子学进展, 2018, 55 (2): 022802, 网络出版: 2018-09-10 复制并引用该论文  本文已被引 10 次  被引通知

图 & 表

图 1. FCN的基本结构

Fig. 1. Basic structure of FCN

下载图片 查看原文

图 2. 网络结构示意图

Fig. 2. Diagram of network structure

下载图片 查看原文

图 3. 区域合并示意图

Fig. 3. Diagram of regional consolidation

下载图片 查看原文

图 4. 分类实验流程图

Fig. 4. Flow chart of classification method

下载图片 查看原文

图 5. 原始影像与标签数据示例。(a)示例1;(b)示例2

Fig. 5. Original images and tag data examples. (a) Example 1; (b) example 2

下载图片 查看原文

图 6. 不同方法的分类结果。(a)原始实验影像;(b)均值漂移①分割结果;(c)均值漂移②分割结果;(d)均值漂移③分割结果;(e)真实分类图;(f) SVM分类结果;(g) ANN分类结果;(h) FCN-16分类结果;(i) FCN-8分类结果;(j) FCN分类结果;(k)加入均值漂移①分割结果的FCN分类结果;(l)加入均值漂移②分割结果的FCN分类结果;(m)加入均值漂移③分割结果的FCN分类结果

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

下载图片 查看原文

图 7. 部分细节标注图。(a)真实分类图;(b) FCN-16分类结果;(c)加入均值漂移②分割结果的FCN分类结果

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

下载图片 查看原文

表 1SVM分类方法的混淆矩阵与总体精度

Table1. 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

查看原文

表 2ANN分类方法的混淆矩阵与总体精度

Table2. 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

查看原文

表 3FCN-16分类方法的混淆矩阵与总体精度

Table3. 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

查看原文

表 4FCN-8分类方法的混淆矩阵与总体精度

Table4. 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

查看原文

表 5所提FCN分类方法的混淆矩阵与总体精度

Table5. 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

查看原文

表 6加入均值漂移①分割结果的FCN分类方法的混淆矩阵与总体精度

Table6. 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

查看原文

表 7加入均值漂移②分割结果的FCN分类方法的混淆矩阵与总体精度

Table7. 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

查看原文

表 8加入均值漂移分割结果③的FCN分类方法的混淆矩阵与总体精度

Table8. 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

查看原文

方旭, 王光辉, 杨化超, 刘慧杰, 闫立波. 结合均值漂移分割与全卷积神经网络的高分辨遥感影像分类[J]. 激光与光电子学进展, 2018, 55(2): 022802. 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.