Application of convolutional neural networks in satellite remote sensing sea ice image classification: A case study of sea ice in the Bohai Sea
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摘要: 本文以TensorFlow为框架搭建卷积神经网络，基于迁移学习的思想，以经典的手写数字识别作为引入，对不同代价函数和激活函数组合对卷积神经网络模型分类结果影响进行了评价分析。以HJ-1A/B渤海海冰图像为实验数据源，分析了不同函数组合对遥感海冰图像分类的影响，优选出交叉熵代价函数与ReLU激活函数为最佳的组合，证明了卷积神经网络在遥感海冰分类中的应用可行性。对渤海海冰图像分类结果进行验证，其中带标签样本验证精度为98.4%。使用该模型对无标签的测试样本进行识别，讨论了样本的窗口尺寸对海冰分类结果的影响，发现在400×400小范围分类实验中最佳窗口尺寸为2×2；最后对整个渤海海域进行识别验证，效果较好。
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关键词:
- 卷积神经网络 /
- 海冰分类 /
- 代价函数 /
- 激活函数 /
- TensorBoard
Abstract: This paper constructs a convolutional neural network based on TensorFlow. According to the idea of migration learning, the classical handwritten digit recognition is introduced as an introduction. The influence of different cost functions and activation function combinations on the classification results of convolutional neural network models is evaluated. Taking HJ-1A/B sea ice images as experimental data source, we analysis the influence of different function combinations on remote sensing sea ice image classification. It turns out that the cross-entropy cost function and the ReLU activation function are optimally combined. The feasibility of CNN in remote sensing sea ice classification is proved, and the classification results of the sea ice images in the Bohai Sea are verified. The calibration accuracy of the labeled samples is 98.4%. The model is then used to identify the unlabeled test samples. The influence of the window size on the sea ice classification results is discussed, and the optimal window size is 2×2 in the 400×400 small-scale classification experiment. Finally, the identification and verification of the entire Bohai Sea area is carried out, and the effect is good.-
Key words:
- CNN /
- sea ice classification /
- cost function /
- activation function /
- TensorBoard
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图 8 模型测试样本400×400数据源（a）和2×2（b）、5×5（c）、10×10（d）窗口大小模型识别结果
a中亮色为海冰，暗色为海水；b−d中紫色代表海冰，黄色代表海水
Fig. 8 Test sample 400×400 (a), and 2×2 (b)、5×5 (c)、10×10 (d) model recognition results
The bright represents sea ice, and the dark represents sea water in a；the purple represents sea ice, and the yellow represents sea water in b-d
图 9 HJ-1B卫星图像20×20（a）、40×40（b）和80×80（c）窗口大小模型识别结果
红色曲线表示冰、水分界线
Fig. 9 20×20 (a)、40×40 (b)、80×80（c）model recognition results of HJ-1B satellite image
The red curve represents the ice-water boundary
图 10 HJ-1A卫星图像20×20（a）、40×40（b）和80×80（c）窗口大小模型识别结果
红色曲线表示冰、水分界线
Fig. 10 20×20 (a)、40×40 (b)、80×80（c）model recognition results of HJ-1A satellite image
The red curve represents the ice-water boundary
表 1 CCD载荷参数
Tab. 1 CCD parameters
有效载荷 波段号 光谱范围/μm 空间分辨率/m 幅宽/km CCD相机 B01 0.43～0.52 30 360(单台) B02 0.52～0.60 30 360(单台) B03 0.63～0.69 30 700(两台) B04 0.76～0.90 30 700(两台) 
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表 2 交叉熵代价函数与ReLU激活函数组合
Tab. 2 Combination of cross-entropy cost function and ReLU activation function
迭代次数 训练精度/% 验证精度/% 8 000 92.0 91.4 10 000 98.0 93.0 20 000 98.0 96.8
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表 5 二次代价函数与Sigmoid激活函数组合
Tab. 5 Combination of quadratic cost function and Sigmoid activation function
迭代次数 训练精度/% 验证精度/% 8 000 30.0 27.0 10 000 44.0 38.4 20 000 74.0 65.3
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表 3 二次代价函数与ReLU激活函数组合
Tab. 3 Combination of quadratic cost function and ReLU activation function
迭代次数 训练精度/% 验证精度/% 8 000 70.0 76.5 10 000 74.0 84.6 20 000 84.0 91.9
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表 4 交叉熵代价函数与Sigmoid激活函数组合
Tab. 4 Combination of cross-entropy cost function and Sigmoid activation function
迭代次数 训练精度/% 验证精度/% 8 000 58.0 45.1 10 000 52.0 51.7 20 000 74.0 83.9
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表 6 不同代价函数和激活函数组合的海冰图像分类结果
Tab. 6 Sea ice image classification results with different cost function and activation function combinations
函数组合 迭代次数 训练精度/% 验证精度/% 交叉熵代价函数与ReLU激活函数组合 50 99.6 98.4 交叉熵代价函数与Sigmoid激活函数组合 50 89.8 80.8
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