摘要
遥感卫星技术的发展使得高空间分辨率的遥感图像得到了更广泛的应用。然而,由于高空间分辨率遥感图像通常具有高的类内方差,因此限制了许多遥感信息提取方法的性能。为解决此类问题,遥感图像中像素间的空间约束成为研究热点,并取得了一些研究成果。整体上,这些成果缺少联系和系统性。鉴于此,本文基于近20年来发表的100余篇相关文献,对现有空间约束流程、应用场景和方法进行了归纳与总结,对各类空间约束方法进行了原理解释及优缺点的比较。最后,本文分析了空间约束方法的发展趋势,列举了空间约束研究存在的不足,并对空间约束方法的研究提出了建议。
The problem of high intraclass variance is apparent in Very High spatial Resolution(VHR)remote sensing images.This problem limits the performance of many remote sensing information extraction methods.Consequently,Spatial Constraints(SCs)within image pixels have become a hot topic,resulting in many research results,but they lack associations and systems orientation from a general perspective.This study reviews and summarizes more than 100 related studies published in the past two decades to provide references for further research on information extraction in VHRs.This study has four sections:In the first section,the three stages of the SCs process(mining and expression of spatial information and construction of the SCs)are described in detail.The primary sources of spatial information were the neighborhood of pixels,imaging relations,and prior knowledge.The spatial information included the mean,median,extreme,and azimuth order.The SCs construction methods included objective functions,energy functions,and discriminant functions.In the second section,the SCs applications are divided into six scenarios(image matching,image segmentation,target detection,image classification,change detection,and others),and the implementation methods and characteristics of the main application scenarios are summarized.The SCs method is closely related to the specific application of the material.For example,SCs is mainly used to build descriptors and perform transformations in image matching;is implemented by model constraints,graph construction in space,and objective functions in image segmentation,target detection and image classification;and emphasizes the neighborhood between pixels and prior knowledge in change detection.The common feature of these scenarios is the development of a robust,unique,and representative descriptor via geometric space information,which can solve specific problems in images.In the third section,the SCs methods are divided into six types according to their implementation and principles(local templates,auxiliary references,spatial graph construction,model constraints,rule constraints,and others),and the advantages and disadvantages of the first five methods are compared.The results showed that the different SCs methods exhibited varying usability across application scenarios.(1)A local template uses the spatial information of the neighborhood and obtains more instances of stable information expression;thus,this approach is suitable for many application scenarios,especially image classification.(2)The point constraint in the auxiliary reference method relies on the spatial relations between feature points and often appears in image matching,while line constraints focus on the connection between the target and the linear object.Thus,this approach is suitable for extracting anthropogenic objects.Furthermore,surface constraints are spatially extensible and suitable for target detection.(3)Graph construction in space can intuitively and effectively extract multidimensional spatial information and is suitable for classifying hyperspectral images.(4)Model constraints are generalized in practical applications but rely on specific mathematical expressions.(5)Rule constraints can specify professional applications and are often used in image classification and change detection.Fully analyzing and considering application scenarios and specific problems are necessary for ensuring the effectiveness of SCs tools.In the fourth section,the development trends and possible shortcomings of SCs research are discussed.Specific suggestions for future work are also provided.
作者
沈宇臻
玉院和
韦玉春
郭厚财
芮旭东
SHEN Yuzhen;YU Yuanhe;WEI Yuchun;GUO Houcai;RUI Xudong(Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China;School of Geography,Nanjing Normal University,Nanjing 210023,China;Key Laboratory of Virtual Geographic Environment(Nanjing Normal University),Ministry of Education,Nanjing 210023,China)
出处
《遥感学报》
EI
CSCD
北大核心
2024年第4期843-859,共17页
NATIONAL REMOTE SENSING BULLETIN
基金
国家自然科学基金(编号:41471283)
江苏省研究生科研与实践创新计划项目(编号:KYCX22_1574)。
关键词
空间约束
遥感图像
信息提取
邻域
辅助约束
遥感变化检测
目标提取
地表覆盖
spatial constraint
remote sensing image
information extraction
neighborhood
auxiliary constraint
remote sensing change detection
target extraction
land cover
作者简介
第一作者:沈宇臻,研究方向为遥感数字图像处理与分析。E-mail:yuzhenshen@nnu.edu.cn;通信作者:韦玉春,研究方向为环境遥感与地理建模。E-mail:weiyuchun@njnu.edu.cn。
