Application of label clustering loss in the classification of remote sensing images

doi:10.6046/zrzyyg.2021147

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Abstract

Scene information of remote sensing images has important application value in image interpretation and actual production and life in various fields. In view of the characteristics of remote sensing images with large intra-class differences and small inter-class differences, this paper further studies the center loss function and proposes a new label clustering loss function. Firstly, the class center is initialized by using the class label center initialization method. Secondly, the sinusoidal attenuation learning rate is used to keep the stability of the class center in the preheating stage. Then, Euclidean distance and cosine distance are used to gather the intra-class features and keep them away from the class center. Furthermore, two network models, VGG16 and ResNet50, are used to verify on NWPU-RESISC45 data set, and the accuracy is improved by 2.3% and 5.7% respectively. Experiments show that the method proposed in this paper can effectively cluster the features and separate class centers, and improve the accuracy of the network model, which has a certain development prospect in the classification of remote sensing images.

Keywords parameter initialization remote sensing image loss function distance between classes intra-class distance

Corresponding Authors: YU Haipeng E-mail: 774052037@qq.com;1377951230@qq.com

Issue Date: 20 June 2022

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	Fu SU
	Haipeng YU
	Weixi ZHU

Cite this article:

Fu SU,Haipeng YU,Weixi ZHU. Application of label clustering loss in the classification of remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(2): 144-151.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021147 OR https://www.gtzyyg.com/EN/Y2022/V34/I2/144

Fig.1 Sine attenuation learning rate

算法1:标签聚类损失的正向反向传播算法

1:输入:网络模型最后一层全连接层特征值{

x i

},训练批次大小m,迭代次数T,学习率

μ

和∂_s
2:初始化: softmax损失参数

θ

,类标签中心参数

c 0

3: for t=1 to T do
4:计算联合损失:

L t o t a l = L s o f t m a x + λ L c

5:更新softmax损失参数:

θ t + 1 = θ t - μ ∂ L t s o f t m a x ∂ θ t

6:更新类别中心:

c j t + 1 = c j t - ∂ s Δ c j t

7:更新模型反向传播误差:

∂ L t t o t a l ∂ x i t = ∂ L t s o f t m a x ∂ x i t + λ ∂ L t c ∂ x i t

8: end for

Tab.1 Label clustering loss training process

Fig.2 Sample dataset of NWPU-RESISC45

Fig.3 Training step diagram of different loss function

Fig.4 Hyperparametric search graph for different network models

Tab.2 Test results of different loss functions in different network models

Fig.5 Two dimensional feature map of VGG16 network model with different loss functions

Fig.6 Comparison of accuracy of different loss functions and scenarios

Tab.3 Classification accuracy of different methods

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