Exploiting low dimensional features from the MobileNets for remote sensing image retrieval
Exploiting low dimensional features from the MobileNets for remote sensing image retrieval
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DOI：
10.1007/s12145-020-00484-3
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发表时间：
2020-06
影响因子：
2.8
通讯作者：
Dongyang Hou;Z. Miao;H. Xing;Hao Wu
中科院分区：
文献类型：
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作者：
Dongyang Hou;Z. Miao;H. Xing;Hao Wu
传统的卷积神经网络（CNN）模型在基于内容的遥感图像检索（CBRSIR）中训练时间长，输出的特征维数高。本文旨在研究MobileNets模型的检索性能，并通过改变最终全连接层的维度来学习CBRSIR的低维表示来对其进行微调。实验结果表明，MobileNets模型在检索准确率和训练速度方面取得了最好的检索性能，与次优模型ResNet 152相比，平均精度提高了11.2%~ 44.39%。此外，微调后的MobileNet的32维特征比原MobileNet和主成分分析方法的检索性能更好，平均精度最大提高分别为11.56%和9.8%。总的来说，MobileNets和提出的微调模型很简单，但与常用的CNN模型相比，它们确实可以大大提高检索性能。
Generally, traditional convolutional neural networks (CNN) models require a long training time and output high-dimensional features for content-based remote sensing image retrieval (CBRSIR). This paper aims to examine the retrieval performance of the MobileNets model and fine-tune it by changing the dimensions of the final fully connected layer to learn low dimensional representations for CBRSIR. Experimental results show that the MobileNets model achieves the best retrieval performance in term of retrieval accuracy and training speed, and the improvement of mean average precision is between 11.2% and 44.39% compared with the next best model ResNet152. Besides, 32-dimensional features of the fine-tuning MobileNet reach better retrieval performance than the original MobileNets and the principal component analysis method, and the maximum improvement of mean average precision is 11.56% and 9.8%, respectively. Overall, the MobileNets and the proposed fine-tuning models are simple, but they can indeed greatly improve retrieval performance compared with the commonly used CNN models.