摘要
单纯借鉴图像处理、计算机视觉技术,难以从根本上解决遥感地学分析问题.为此,以面向对象影像分析范式为例,剖析区域、尺度、格局与功能的地理学原理,将遥感影像地学分析视为基于遥感影像信息的精细尺度下地理空间区域划分、等级结构表达、空间结构推断空间功能的过程.在此基础上,提出基于等级斑块建模的循环迭代式遥感影像地学分析框架.该框架,首先由遥感影像与其它地理信息数据和知识共同构建能够表达地理空间的等级斑块模型;然后,依托等级斑块模型的上下层次关系和遥感影像特征,协同实现对象的精准识别;进而,使用精准识别结果更新等级斑块模型,提供更为精准的上下层次关系特征,从而实现后续迭代过程中更高精度的识别.此外,还提出了实现该框架的10条建议,例如为不同地理要素构建不同的等级斑块模型.该框架提供了一种地学知识引导下的遥感大数据智能解译思路,有望实现地学知识自动积累更新和遥感智能解译精度提升的协同互促.
Abstract
In the past two decades,Geographical Object-Based Image Analysis(GEOBIA)has been widely studied and applied;however,it still does not meet the expectation for big remote sensing image analysis in geographical cognition activities in terms of accuracy and intelligence.We think that the major problem is the lack of geographical thoughts to lead the research and development(R&D)of GEOBIA key techniques,especially when introducing the techniques of computer vision,which does not regard comprehending the earth's surface as the objective.On this basis,we review the concepts of GEOBIA from a geographical perspective,specifically the principles of region,scale,and pattern and function.From the region principle,we regard the image segmentation in GEOBIA grouping the spatial neighbor pixels sharing similar spectral and textures as the representation of a fine-scale geographical zoning in remote sensing image spaces.From the scale principle,we regard the multiscale of segmentation as the representation model quantifying the relationship of geographical zones among different scales.From the pattern and function principle,we regard the multiscale segmentation as an ideal hierarchical patch model representing the earth surface structure,i.e.,the landscape,and could quantify the pattern(e.g.,orientation,shape,arrangement,distance,etc.)for the function recognition.In other words,we think that the target of GEOBIA is to recover the hierarchical multiscale structure of the earth's surface from the remote sensing images so that we can quantify the structure and then recognize and comprehend its function.On the basis of these reviews,we propose an iterative GEOBIA framework where the core is constructing a hierarchical patch model of the earth's surface.The framework starts with fusing the big geographical data,including remote sensing images,existing geographical thematic maps,and other helpful knowledge,to construct an initial hierarchical patch model of the earth's surface.Then,object features are extracted from the hierarchical patch model,and the function of these objects is recognized;the features include the internal features extracted from the object itself(e.g.,shape and spectrum)and the external features extracted from its relationship with other objects(e.g.,its neighbor objects,parent objects,and children objects).Finally,the recognized results are used to update the hierarchical patch model for the next recognition cycles.With the iteration of the remote sensing image analysis,the accuracy of geographical object recognition can be improved because we also have a more accurate hierarchical patch model describing the earth's surface due to the updating process,which could provide an accurate calculation of the object's features.To achieve the above proposed framework,we also propose a few suggestions for further R&D,such as constructing different hierarchical patch models for different geographical elements,fusing multiresolution images by using the hierarchical patch model instead of pixels,and choosing the suitable interpretation models instead of one model for different big geographical patches.We hope the above insights could provide an instructive idea of how to embed geographical knowledge into intelligent interpretation models to extract new knowledge from big remote sensing images with improved accuracy.
基金项目
国家重点研发计划(2021YFB3900501)
国家自然科学基金(41890854)
国家自然科学基金(41901354)
资源与环境信息系统国家重点实验室自主创新项目(KPI001)
资源与环境信息系统国家重点实验室自主创新项目(YPI004)
NETL
NSTL
万方数据