Remote sensing image retrieval with ant colony optimization and a weighted image-to-class distance

被引：0

作者：

Ye F. ^{[1
]}

Meng X. ^{[1
]}

Dong M. ^{[2
]}

Nie Y. ^{[1
]}

Ge Y. ^{[3
]}

Chen X. ^{[1
]}

机构：

[1] School of Surveying and Mapping Engineering, East China University of Technology, Nanchang

[2] School of Information Engineering, Nanchang University, Nanchang

[3] School of Software, Nanchang Hangkong University, Nanchang

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2021年 / 50卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Ant colony optimization; Convolutional neural networks; Image-to-class similarity; Pheromone; Remote sensing image retrieval;

D O I：

10.11947/j.AGCS.2021.20200357

中图分类号：

学科分类号：

摘要：

Remote sensing image retrieval (RSIR) aims to find relevant images of a query image from a remote sensing image retrieval dataset. But the similarity between a query image and a retrieval image is generally used and the relationship among images on the retrieval dataset is neglected during the retrieval process. To deal with the problem, this paper presents a new retrieval method based on ant colony optimization (ACO) for RSIR. First, our method uses the pheromone to represent the similarity between images on the retrieval dataset; then the pheromone matrix is updated by ACO. Finally, the pheromone of images is used to improve the performance of RSIR. Meanwhile, an improved weighted image-to-class distance is used to measure the similarity between two images for further improving the retrieval performance. Extensive experiments are conducted on two publicly available remote sensing image databases, UCMD and PatternNet. Compared with the state-of-the-art methods, the proposed method can achieve better retrieval results. © 2021, Surveying and Mapping Press. All right reserved.

引用

页码：612 / 620

页数：8

共 31 条

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