计算机应用与软件2024，Vol.41Issue(6)：181-185,199,6.DOI:10.3969/j.issn.1000-386x.2024.06.027
基于注意力机制和软匹配的多标签遥感图像检索方法
A MULTI-LABEL REMOTE SENSING IMAGE RETRIEVAL METHOD BASED ON ATTENTION MECHANISM AND SOFT MATCHING
摘要
Abstract
To address the problems that convolutional neural networks are weak in extracting features of multi-label remote sensing images and the reflection of complexity multiple labels in remote sensing images,a multi-label remote sensing image retrieval method based on attention mechanism and soft matching is proposed.In the feature extraction stage,the method was based on the densely connected convolutional neural networks,and a CBAM(Convolutional Block Attention Module)layer was added after each dense block to achieve feature extraction of multi-label image regions.During model training,the joint loss function that distinguished hard matching and soft matching was used to learn the Hash code representation of the images.The retrieval results were obtained by evaluating the Hamming distance between the image Hash code and the retrieved image Hash code.The experimental results show the proposed method has a significant improvement in retrieval accuracy compared with other deep hashing methods based on global features on the universal dataset NUS-WIDE and multi-label remote sensing image dataset DLRSD.关键词
遥感图像检索/密集卷积神经网络/深度哈希/多标签/软匹配Key words
Remote sensing image retrieval/Densely connected convolutional neural networks/Deep hashing/Multi-label/Soft matching分类
信息技术与安全科学引用本文复制引用
张永梅,徐敏,李小冬..基于注意力机制和软匹配的多标签遥感图像检索方法[J].计算机应用与软件,2024,41(6):181-185,199,6.基金项目
国家自然科学基金项目(61371143) （61371143）
教育部科技发展中心"天诚汇智"创新促教基金项目(2018A03029). （2018A03029）
