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

doi:10.6046/zrzyyg.2024206

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摘要

针对当前高分辨率遥感影像场景复杂难以简单解析，且变化多样难以从样本库获取准确参考的问题，文章参照视觉的双驱动认知机理，提出了一种自学习的高分辨率遥感影像分割方法。该方法在借鉴视觉感知原理的基础上，通过非监督的自适应分析解读场景中的典型地物，并结合神经网络实现典型地物的自学习辨识，最后结合非监督分析与神经网络学习实施分割结果的自检校修正。采用包含复杂地面场景的真实高分辨率遥感影像数据，对比2种目前流行的深度神经网络分割方法Mask R-CNN （mask region-based convolutional neural network，MR）和ScalableViT （scalable vision Transformers，SViT）进行实验，实验结果表明所提方法能保持稳健、可靠的分割精度，在地物认知、泛化性能和抗干扰能力方面具有显著优势，是一种性价比高、实用性强的方法。

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	吴志军
	丛铭
	许妙忠
	韩玲
	崔建军
	赵超英
	席江波
	杨成生
	丁明涛
	任超锋
	顾俊凯
	彭晓东
	陶翊婷

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

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.

Key words： bionic visual high-resolution remote sensing image segmentation unsupervised analysis deep learning neural network self-learning method

收稿日期: 2024-06-12 出版日期: 2025-10-28

ZTFLH:	TP753
	P237

基金资助: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”(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”(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”(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”(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”(SXLK2021-0225);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”(42171348)

通讯作者: 丛铭（1987-），男，博士，主要从事遥感影像分析领域的研究。Email：kul_chd@qq.com。

作者简介: 吴志军（2001-），男，硕士研究生，主要从事图像处理方面的研究。Email：3408419816@qq.com。

引用本文:

吴志军, 丛铭, 许妙忠, 韩玲, 崔建军, 赵超英, 席江波, 杨成生, 丁明涛, 任超锋, 顾俊凯, 彭晓东, 陶翊婷. 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.
WU Zhijun, CONG Ming, XU Miaozhong, HAN Ling, CUI Jianjun, ZHAO Chaoying, XI Jiangbo, YANG Chengsheng, DING Mingtao, REN Chaofeng, GU Junkai, PENG Xiaodong, TAO Yiting. Self-learning segmentation of high-resolution remote sensing images based on visual dual-drive cognition. Remote Sensing for Natural Resources, 2025, 37(5): 73-90.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2024206 或 https://www.gtzyyg.com/CN/Y2025/V37/I5/73

Fig.1 模拟视觉双驱动认知的自适应分割思路

Fig.2 独立地物的多尺度表征示例图

Fig.3 超像素聚类形成同源可靠样本示例图

Fig.4 特征挖掘结构

Fig.5 自注意权重分配结构

Fig.6 多尺度差分编码结构

Fig.7 多重感受野解码结构

Fig.8 整体网络结构

Fig.9 错误修复思路

Fig.10 模拟视觉双驱动认知的自适应分割流程

Fig.11 实验一分割结果

Tab.1 实验一的分割精度

Tab.2 实验二的分割精度

Fig.12 实验二分割结果

Fig.13 实验三分割结果

Tab.3 实验三的分割精度

Tab.4 实验四的分割精度

Fig.14 实验四分割结果

Tab.5 实验五的分割精度

Fig.15 实验五分割结果

Tab.6 实验六的分割精度

Fig.16 实验六分割结果

Fig.17 3种方法的精度对比图

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