面向高分辨率遥感影像变化检测的混合空间金字塔池化网络  

Hybrid spatial pyramid pooling network to change detection for high resolution remote sensing images

作　　者：邵攀 高梓昂 SHAO Pan;GAO Zi’ang(Hubei Key Laboratory of Intelligent Vision Monitoring for Hydroelectric Engineering,China Three Gorges University,Yichang 443002,China;College of Computer and Information Technology,China Three Gores University,Yichang 443002,China)

机构地区：[1]三峡大学湖北省水电工程智能视觉监测重点实验室,宜昌443002 [2]三峡大学计算机与信息学院,宜昌443002

出　　处：《遥感学报》2025年第1期279-289,共11页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:41901341)。

摘　　要：以残差网络为基础,结合高分辨率遥感影像的特点,提出一种全新的端到端变化检测网络——混合空间金字塔池化网络HSPPNet(Hybrid Spatial Pyramid Pooling Network)。HSPPNet首先将空洞卷积和注意力机制引导卷积并行集成,构建一种混合空间金字塔池化模块,以便有效提取高分辨率遥感影像中不同形状尺度的变化对象。然后,通过定义一种截断—补偿加权交叉熵函数和一种类别级IoU函数并将二者集成,得到一种全新的自适应平衡损失函数,来降低变化类与未变化类严重不均衡问题对变化检测的影响。最后设计一种简单有效的输入模块,通过综合考虑两期遥感影像及其差异图来增强变化信息。通过以上3点,HSPPNet增强了深度学习变化检测的性能。两组常用公开变化检测数据集上的实验结果表明HSPPNet可行、有效。Change Detection(CD)is the process of identifying the land cover changes of interest by analyzing bitemporal remote sensing images acquired in the same geographical area at different times.As a popular research topic in remote sensing,CD plays an important role in many practical applications,such as urban research,environmental monitoring,and disaster assessment.With the rapid development of deep learning,its applications have expanded to the remote sensing CD task,leading to the development of numerous deep learning-based CD methods.Although the existing deep learning-based CD methods have their own advantages and can implement the CD task effectively,their applicability and robustness need to be further analyzed.How to design an appropriate network model to obtain more reliable and accurate CD results is still an open problem.Currently,two main challenges remain for CD:(1)The changed objects existing in remote sensing images have various scales and shapes.(2)The proportions of the changed and unchanged classes are often significantly unbalanced,i.e.,the number of the changed pixels is usually much less than that of the unchanged pixels.To deal with these two challenges,this study proposes a novel end-to-end CD network for high-resolution remote sensing images based on a residual network,i.e.,Hybrid Spatial Pyramid Pooling Network(HSPPNet).First,a Hybrid Spatial Pyramid Pooling(HSPP)module is built by integrating atrous convolutions and attention mechanism-guided convolutions in parallel to effectively extract the changed objects with different shapes and scales from high-resolution remote sensing images.Then,an adaptive balancing loss function is presented to alleviate the effects of the serious imbalance between changed and unchanged classes on CD.The loss function is constructed by defining a truncation–compensation weighting cross entropy function and a class-level IoU function and integrating them.Finally,a simple but effective input module that considers the bitemporal remote sensing images and their dif

关 键 词：遥感 变化检测 深度学习 混合空间金字塔池化 注意力机制 损失函数 截断—补偿加权交叉熵 类别级IoU 

分 类 号：P237[天文地球—摄影测量与遥感] P2[天文地球—测绘科学与技术]