A Double Knowledge Distillation Model for Remote Sensing Image Scene Classification
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摘要: 为了提高轻型卷积神经网络(CNN)在遥感图像(RSI)场景分类任务中的精度，该文设计一个双注意力(DA)与空间结构(SS)相融合的双知识蒸馏(DKD)模型。首先，构造新的DA模块，将其嵌入到ResNet101与设计的轻型CNN，分别作为教师与学生网络；然后，构造DA蒸馏损失函数，将教师网络中的DA知识迁移到学生网络之中，从而增强其对RSI的局部特征提取能力；最后，构造SS蒸馏损失函数，将教师网络中的语义提取能力以空间结构的形式迁移到学生网络，以增强其对RSI的高层语义表示能力。基于两个标准数据集AID和NWPU-45的对比实验结果表明，在训练比例为20%的情况下，经知识蒸馏之后的学生网络性能分别提高了7.69%和7.39%，且在参量更少的情况下性能也优于其他方法。Abstract: In order to improve the accuracy of light-weight Convolutional Neural Networks (CNN) in the classification task of Remote Sensing Images (RSI) scene, a Double Knowledge Distillation (DKD) model combined with Dual-Attention (DA) and Spatial Structure (SS) is designed in this paper. First, new DA and SS modules are constructed and introduced into ResNet101 and lightweight CNN designed as teacher and student networks respectively. Then, a DA distillation loss function is constructed to transfer DA knowledge from teacher network to student network, so as to enhance their ability to extract local features from RSI. Finally, constructing a SS distillation loss function, migrating the semantic extraction ability in the teacher network to the student network in the form of a spatial structure to enhance its ability to express the high -level semantics of the RSI. The experimental results based on two standard data sets AID and NWPU-45 show that the performance of the student network after knowledge distillation is improved by 7.57% and 7.28% respectively under the condition of 20% training proportion, and the performance is still better than other methods under the condition of fewer parameters.
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表 1 学生网络具体参数设计
网络层名 输出尺寸 计算方法 Conv1 112×112 7×7，64，stride=2 DA 112×112 DA模块 Conv2_x 56×56 3×3 max pool, stride=2 [3×3, 64; 3×3,64] Conv3_x 28×28 [3×3, 128; 3×3,64] Conv4_x 14×14 [3×3, 256; 3×3,64] Conv5_x 7×7 [3×3, 512; 3×3,64] 1×1 average pool,45-d fc, softmax 
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算法1 双知识蒸馏(DKD)学生网络训练及测试 输入：训练图像$ D = \{ ({\text{IM}}{{\text{G}}_n},{y_n}):n = 1,2, \cdots ,N\} $，网络超参
(Epoches, BS与lr)，测试图像
$ {\text{Tst}} = \{ ({\text{IM}}{{\text{G}}_m},{y_m}):m = 1,2, \cdots ,M\} $输出：学生网络参数${\varOmega _{\text{S} } }$及测试图像分类精度 准备：将D中的训练图像组成3元组，采用图3所示孪生框架训练
教师网络${\varOmega ^{ {\text{TE} } } }$；For epoch in Epoches: (1) 根据批大小BS，对D中的训练图像进行分批； (2) 每批图像送入教师网络${\varOmega ^{ {\text{TE} } } }$，得到的高层语义特征
${\text{Tb}} = \{ {t_i}|i = 1,2, \cdots ,{\text{BS}}\} $；(3) 每批图像送入学生网络${\varOmega _{\text{S} } }$，得到的高层语义特征
${\text{Sb}} = \{ {s_i}|i = 1,2, \cdots ,{\text{BS}}\} $及预测标签$\{ {\tilde y_i}\} _{i = 1}^{{\text{BS}}}$；(4) 用式(15)计算${L_{{\text{HTL}}}}$，优化器通过反向传播更新学生网络
参数${\varOmega _{\text{S} } }$;(5) 采用余弦衰减策略更新学习率lr。 End for (6) 对 $ \forall {\text{IM}}{{\text{G}}_m} \in {\text{Tst}} $，将${\text{IM}}{{\text{G}}_m}$输入学生网络${\varOmega _{\text{S} } }$，得到其
类别预测结果${ { {\tilde y} }_{{m} } }$；(7) 根据$ \{ ({\bar y_m},{y_m}):m = 1,2, \cdots ,M\} $，统计分类精度且输出。
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表 2 不同训练比例下消融实验的OA值(%)
AID训练比例(%) NWPU-45训练比例(%) 20 50 10 20 基线 87.52 89.43 86.27 88.48 +DA 93.08 94.36 91.68 93.65 +SS 93.92 94.63 92.91 94.12 +DKD 95.21 97.04 93.88 95.87 教师 95.93 97.63 94.47 96.52
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表 4 基于AID与NWPU-45数据集的综合对比实验结果(%)
方法 AID训练比例(%) NWPU-45训练比例(%) 20 50 10 20 VGG16+MSCP[22] 91.52 94.42 88.32 91.56 ARCNet-VGG[19] 88.75 93.10 85.60 90.87 CNN-CapsNet[23] 93.79 96.32 89.03 89.03 SCCov[24] 93.12 96.10 89.30 92.10 GBNet[25] 92.20 95.48 90.03 92.35 MF2Net[26] 93.82 95.93 90.17 92.73 MobileNet[20] 88.53 90.91 80.32 83.26 ViT-B-16[21] 93.81 95.90 90.96 93.36 XU et al.[27] 94.17 96.19 90.23 93.25 DKD (本文) 95.21 97.04 93.88 95.87
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