自然资源遥感2025，Vol.37Issue(5)：73-90,18.DOI:10.6046/zrzyyg.2024206
基于视觉双驱动认知的高分辨率遥感影像自学习分割方法
Self-learning segmentation of high-resolution remote sensing images based on visual dual-drive cognition
摘要
Abstract
The current high-resolution remote sensing images involve complex scenes that are difficult to analyze.Meanwhile,owing to the diverse scenes,there is a lack of accurate reference obtained from the sample database.Therefore,this paper proposed a self-learning segmentation method for high-resolution remote sensing images,with reference to the visual dual-drive cognition mechanism.Based on the principle of visual perception,this method interpreted the typical ground objects in the scene through unsupervised adaptive analysis.In addition,it achieved self-learning identification of typical ground objects by integrating a neural network.Finally,the segmentation results were self-checked and corrected by combining unsupervised analysis and neural network learning.Using real high-resolution remote sensing image data containing complex ground scenes,the comparative experiments were conducted between the proposed method and two popular deep neural network segmentation methods:mask region-based convolutional neural network(Mask R-CNN)and scalable vision transformer(ScalableViT).The results showed that the proposed method can maintain robust and reliable segmentation accuracy,and outperformed others in terms of ground object cognition,generalization performance,and anti-interference ability.As such,it proved to be a cost-effective and practical approach.关键词
视觉仿生/高分辨率遥感/影像分割/非监督分析/深度学习神经网络/自学习方法Key words
bionic visual/high-resolution remote sensing/image segmentation/unsupervised analysis/deep learn-ing neural network/self-learning method分类
计算机与自动化引用本文复制引用
吴志军,任超锋,顾俊凯,彭晓东,陶翊婷,丛铭,许妙忠,韩玲,崔建军,赵超英,席江波,杨成生,丁明涛..基于视觉双驱动认知的高分辨率遥感影像自学习分割方法[J].自然资源遥感,2025,37(5):73-90,18.基金项目
陕西省教育厅服务地方专项计划项目"工程外部空间遥感信息获取、建模、解译与信息智慧管控关键技术研究"(编号:23JE002)、国家科技部的国家重点研发计划项目"陆路交通基础设施智能化设计共性关键技术"课题一"北斗定位与空天地集成高精度智能测绘技术"(编号:2021YFB2600401)、国家自然科学基金项目"基于动态宽度与深度学习的多源异构数据下高山峡谷区链生地质灾害智能识别研究"(编号:42371356)、国家重点研发计划子课题"重大崩滑隐患多源精准识别与InSAR精细监测技术及应用示范"(编号:2021YFC3000404-01)、陕西省林业科技创新计划专项"基于高光谱遥感深度学习的林地增损监测技术研究"(编号:SXLK2021-0225)和国家自然科学基金项目"基于多源异构时空数据融合的黄土区滑坡智能识别研究"(编号:42171348)共同资助. （编号:23JE002）
