葛芸^1,,,
马琳¹,
江顺亮²,
叶发茂²
1.南昌航空大学软件学院 南昌 330063
2.南昌大学信息工程学院 南昌 330031
基金项目:国家自然科学基金(41801288, 41261091, 61662044, 61663031, 61762067)

详细信息

作者简介:葛芸：女，1983年生，博士，讲师，研究方向为遥感图像处理与机器学习
马琳：女，1996年生，硕士生，研究方向为遥感图像处理与机器学习
江顺亮：男，1966年生，博士，教授，博士生导师，研究方向为算法设计与分析、计算机模拟与仿真、机器视觉
叶发茂：男，1978年生，博士，副教授，研究方向为遥感图像处理、计算机图形学、机器学习

通讯作者:葛芸　geyun@nchu.edu.cn

中图分类号:TP751.1

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出版历程

收稿日期:2019-01-09
修回日期:2019-06-18
网络出版日期:2019-06-25
刊出日期:2019-10-01

The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval

Yun GE^1,,,
Lin MA¹,
Shunliang JIANG²,
Famao YE²
1. School of Software, Nanchang Hangkong University, Nanchang 330063, China
2. School of Information Engineering, Nanchang University, Nanchang 330031, China
Funds:The National Natural Science Foundation of China (41801288, 41261091, 61662044, 61663031, 61762067)


摘要
摘要:高分辨率遥感图像内容复杂，提取特征来准确地表达图像内容是提高检索性能的关键。卷积神经网络(CNN)迁移学习能力强，其高层特征能够有效迁移到高分辨率遥感图像中。为了充分利用高层特征的优点，该文提出一种基于高层特征图组合及池化的方法来融合不同CNN中的高层特征。首先将高层特征作为特殊的卷积层特征，进而在不同输入尺寸下保留高层输出的特征图；然后将不同高层输出的特征图组合成一个更大的特征图，以综合不同CNN学习到的特征；接着采用最大池化的方法对组合特征图进行压缩，提取特征图中的显著特征；最后，采用主成分分析(PCA)来降低显著特征的冗余度。实验结果表明，与现有检索方法相比，该方法提取的特征在检索效率和准确率上都有优势。
关键词:遥感图像检索/
迁移学习/
高层特征图/
组合/
池化
Abstract:High-resolution remote sensing images have complex visual contents, and extracting feature to represent image content accurately is the key to improving image retrieval performance. Convolutional Neural Networks (CNN) have strong transfer learning ability, and the high-level features of CNN can be efficiently transferred to high-resolution remote sensing images. In order to make full use of the advantages of high-level features, a combination and pooling method based on high-level feature maps is proposed to fuse high-level features from different CNNs. Firstly, the high-level features are adopted as special convolutional features to preserve the feature maps of the high-level outputs under different input sizes, and then the feature maps are combined into a larger feature map to integrate the features learned by different CNNs. The combined feature map is compressed by max-pooling method to extract salient features. Finally, the Principal Component Analysis (PCA) is utilized to reduce the redundancy of the salient features. The experimental results show that compared with the existing retrieval methods, the features extracted by this method have advantages in retrieval efficiency and precision.
Key words:Remote sensing image retrieval/
Transfer learning/
High-level feature map/
Combination/
Pooling



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