遥感图像蚁群算法和加权图像到类距离检索法

doi:10.11947/j.AGCS.2021.20200357

摘要/Abstract

摘要： 遥感图像检索目的是从遥感图像库中寻找出与查询图像相关的图像，但在检索过程中一般只考虑查询图像与待检索图像的相似度，通常忽略了遥感图像库中图像之间的语义相似度。针对该问题，本文提出一种基于蚁群算法和改进的加权图像到类距离的遥感图像检索算法。首先利用信息素浓度描述遥感图像库中图像之间的语义相似度，然后采用蚁群算法更新信息素浓度，最后在检索过程中，充分利用图像之间的语义相似度，提升遥感图像检索性能。此外，还改进了一种加权图像到类距离，用于提高度量查询图像与待检索图像间的相似度准确性，从而进一步提升检索性能。在两个公开的遥感图像数据集（UCMD和PatternNet）上的试验结果表明，本文方法能够取得比现有方法更好的检索结果。

关键词: 遥感图像检索, 蚁群算法, 信息素, 图像到类距离, 卷积神经网络

Abstract: 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.

Key words: remote sensing image retrieval, ant colony optimization, pheromone, image-to-class similarity, convolutional neural networks

中图分类号:

P227

叶发茂, 孟祥龙, 董萌, 聂运菊, 葛芸, 陈晓勇. 遥感图像蚁群算法和加权图像到类距离检索法[J]. 测绘学报, 2021, 50(5): 612-620.

YE Famao, MENG Xianglong, DONG Meng, Nie Yunju, GE Yun, CHEN Xiaoyong. Remote sensing image retrieval with ant colony optimization and a weighted image-to-class distance[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(5): 612-620.

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