电讯技术2025，Vol.65Issue(1)：35-42,8.DOI:10.20079/j.issn.1001-893x.231127005
基于双文本提示和多重相似性学习的多标签遥感图像分类
Bi-text Prompts and Multi-similarity Learning for Multi-label Remote Sensing Image Classification
摘要
Abstract
Multi-label remote sensing image classification aims to predict multiple interrelated objects presenting in remote sensing images,where text labels provide rich semantic information.However,most current multi-label image classification methods fall short of adequately considering the information in visual-semantic image-text pairs.To address this issue,a multi-label remote sensing image classification algorithm based on Bi-text Prompts and Multi-similarity(BTPMS)learning is proposed.This algorithm first leverages Bi-text Prompts(BTP)from scene and object label text to provide rich prior knowledge.Subsequently,considering the correlation between scene and object labels,it calculates multi-similarities between obtained text features and image features.Finally,it utilizes similarity scores for multi-label remote sensing image classification.Additionally,a novel Local Feature Attention(LFA)module is designed to capture local structures in images from both spatial and channel dimensions.Extensive experiments on two benchmark remote sensing datasets demonstrate the superiority of the proposed algorithm over comparative multi-label image classification methods.关键词
遥感图像/多标签图像分类/视觉语言预训练/提示学习/局部特征注意力Key words
remote sensing images/multi-label image classification/visual-and-language pretraining/prompt learning/local feature attention分类
信息技术与安全科学引用本文复制引用
白淑芬,宋铁成..基于双文本提示和多重相似性学习的多标签遥感图像分类[J].电讯技术,2025,65(1):35-42,8.基金项目
国家自然科学基金面上项目(62371084) （62371084）
