决策树支持下跨比例尺居民地数据更新

针对现有的跨比例尺地图居民地数据更新方法需要大量人工干预和专家判读选择的问题,该文提出了一种基于决策树的跨比例尺居民地更新方法。利用决策树的判断推理机制对不同变化更新场景下要素的地理环境特征、空间分布关系、尺度表达差异等影响更新行为选择的因素进行综合分析,结合尺度变换技术构建了跨比例尺居民地数据更新模型,较好地顾及了数据更新的合理性及更新效率。基于广州市某区域的真实数据进行试验,结果表明:该方法不仅能够有效地减少更新过程中的人工干预,提高智能化程度,同时能从一定程度上提高更新效率。     

地理信息图库一体更新方法

地图与DLG数据的更新方式之间既有区别又有联系，在研究DLG数据增量更新及地图与DLG数据一体化管理的基础上，提出一种地理信息图库一体更新方法，以地理实体为单位进行几何、属性和地图表达信息组织管理，获取DLG数据的增量信息并对增量信息进行地图表达得到符号图形增量信息。最后通过试验数据验证了该方法的有效性，且其具有易于计算机实现、效率高、可扩展性强、适于地图与DLG数据一体化更新等优点。     

叠置运算支持下的跨比例尺城区居民地数据变化发现与更新

以城区居民地为例,提出了一种面向跨比例尺新旧地图数据变化发现与更新方法。首先,从产生效应、发生源头、依附上下文关系、更新策略4个方面剖析大比例尺新数据与小比例尺旧数据间的变化;在此基础上,引入叠置运算与数据增强方法构建变化信息提炼与融合模型,形成"叠置分析与变化发现→增量提取→增量融合更新"的技术框架;最后,通过试验数据验证表明了方法的有效性,以及可扩展性强、适于图层级批处理更新等优点。     

面向中比例尺公共地图基础数据库缩编方法研究

面向1∶250 000中比例尺公共地图基础数据库和政务用图需求,在构建缩编规则库的基础上,通过定义不同地物要素的缩编与转换规则,经过选取、概括和移位处理后,探索利用1∶50 000基础地图数据库跨比例尺级别进行自动缩编和数据更新的技术流程与方法,实现了大比例尺地图缩编更新获取中比例尺地图的目标。研究结果表明,利用基于规则库的缩编与更新方法,将1∶50 000基础地图数据缩编更新为1∶250 000基础地图数据,其成果精度可靠,完全可以满足中比例尺公共基础数据库快速更新的要求。     

地图数据多比例尺级联更新方式研究

保持地图数据的现势性和一致性是测绘部门的一项主要工作,解决多比例尺地图数据的级联更新正是服务于这一工作的。本文提出了一种地图数据增量式缩编级联更新方式,在原有数据基础上只对发生变化的数据进行相应更新,并将这些变化更新传递到下一级小比例尺数据上,实现多个比例尺数据的级联更新。     

公开版地图数据库的快速更新方法研究

以福建省公开版地图数据库快速更新为例,研究公开版地图数据库快速更新的方法。针对数据精度没有基础地理信息数据库的数据精度高的特点,通过地图服务反馈、各类影像数据、车载导航数据采集等方法和渠道获取变化信息,建立"动态实时更新"的技术流程,同时通过DATE属性提取增量变化信息实现多级比例尺的联动更新。该方法已应用于福建省公开版地图数据库的快速更新,极大地提高了福建省公开版地图数据的现势性与准确性,应急保障出图能力也大大提升,可为其他省提供参考。     

路网矢量地图数据生成实体对象的方法研究

新一代全空间信息系统提出了新的空间数据模型——多粒度时空对象模型,多粒度时空实体对象通过唯一实体标识、多元特征信息记录空间实体,能够较好地支持空间实体的空间和语义表达。矢量地图数据作为现有重要的基础地理空间数据,记录了地理实体具体属性和位置信息,为实体对象生成提供了强大的基础数据支持。本文提出了路网矢量地图数据生成实体道路对象的研究方法,通过地图数据特征分析、实体对象生成策略和关键技术研究、实体对象转换3个方面对矢量地图数据的实体对象生成展开研究,实验表明:本文提出的实体对象转换方法为矢量地图数据生成实体对象提供了一个可行的解决方案。     

基于地形图数据库的跨比例尺级别缩编更新方法研究

针对利用1：500地形图快速更新其他比例尺地形图的需求,本文提出了基于地形图数据库的同比例尺级别和跨比例尺级别全自动缩编更新入库的方法。通过设计地形图模板及过渡模板,定义地物要素综合取舍转换规则,开发自动缩编软件模块,定制自动缩编更新任务,利用数据库中测量更新范围线自动下载地形图数据进行缩编及更新入库,实现大比例尺地形图依次缩编更新获取多种比例尺地形图的目标。本文提出的技术方法已在长沙市城市基础地理信息数据库更新工作中得到了验证,为采用缩编方式快速获取多样性地理信息数据提供了技术经验。     

地理空间数据库联动更新技术

以浙江省地理空间数据交换平台多比例尺、跨行业的地理空间数据整合与更新为背景,介绍了地理空间数据库的联动更新模式,描述了多比例尺数据的级联更新和跨行业数据的协同更新两种模式,以及支撑联动更新的两大关键技术,即实体级增量更新和地理实体编码。联动更新技术在浙江省地理空间数据交换平台的运用与相关机制的建立,对提高平台各类空间数据的更新效率和数据一致性提供了技术保证。     

多尺度地图面目标变化分类、描述及判别

基础地理空间数据的持续更新是制约地理信息服务的重要因素,而变化信息的获取是空间数据持续更新的基础。将不同时相多尺度地图面目标基本变化类型归纳为出现、消失、扩张、收缩、移动、旋转、分裂、合并和先分裂后合并等9种,并分别进行了形式化定义和描述。进而顾及制图综合操作产生的不同尺度地图中同名面目标表现的"伪变化",利用4交差拓扑关系模型区分真实变化和制图综合引起的几何差异。最后,采用1∶2 000和1∶10 000比例尺居民地地图数据进行实验验证,结果表明,所提的变化分类、描述及判别方法具有有效性和适用性。     

A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales

The availability of geospatial data has increased significantly over recent decades. As a result, the question of how to update spatial data across different scales has become an attractive topic. One promising strategy is to use an updated larger‐scale dataset as a reference for detecting and updating changed objects represented in a to‐be‐updated smaller‐scale dataset. For such an update method, an understanding of the different types of changes that can occur is crucial. Using polygonal building data as an example, this study examines the various possible changes from different perspectives, such as the reasons for their occurrence, the forms in which they manifest, and their effects on output. Then, we apply map algebra theory to establish a cartographic model for updating polygonal building data. Supported by concepts of map algebra, an update procedure involving change detection, filtering, and fusion is implemented through a series of set operations. In addition to traditional polygon overlay functions, the constrained Delaunay triangulation model and knowledge of map generalization procedures are employed to construct set operations. The proposed method has been validated through tests using real‐world data. The experimental results show that our method is effective for updating 1:10k map data using 1:2k map data.     

Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery

Due to the fast development of the urban environment, the need for efficient maintenance and updating of 3D building models is ever increasing. Change detection is an essential step to spot the changed area for data (map/3D models) updating and urban monitoring. Traditional methods based on 2D images are no longer suitable for change detection in building scale, owing to the increased spectral variability of the building roofs and larger perspective distortion of the very high resolution (VHR) imagery. Change detection in 3D is increasingly being investigated using airborne laser scanning data or matched Digital Surface Models (DSM), but rare study has been conducted regarding to change detection on 3D city models with VHR images, which is more informative but meanwhile more complicated. This is due to the fact that the 3D models are abstracted geometric representation of the urban reality, while the VHR images record everything. In this paper, a novel method is proposed to detect changes directly on LOD (Level of Detail) 2 building models with VHR spaceborne stereo images from a different date, with particular focus on addressing the special characteristics of the 3D models. In the first step, the 3D building models are projected onto a raster grid, encoded with building object, terrain object, and planar faces. The DSM is extracted from the stereo imagery by hierarchical semi-global matching (SGM). In the second step, a multi-channel change indicator is extracted between the 3D models and stereo images, considering the inherent geometric consistency (IGC), height difference, and texture similarity for each planar face. Each channel of the indicator is then clustered with the Self-organizing Map (SOM), with “change”, “non-change” and “uncertain change” status labeled through a voting strategy. The “uncertain changes” are then determined with a Markov Random Field (MRF) analysis considering the geometric relationship between faces. In the third step, buildings are extracted combining the multispectral images and the DSM by morphological operators, and the new buildings are determined by excluding the verified unchanged buildings from the second step. Both the synthetic experiment with Worldview-2 stereo imagery and the real experiment with IKONOS stereo imagery are carried out to demonstrate the effectiveness of the proposed method. It is shown that the proposed method can be applied as an effective way to monitoring the building changes, as well as updating 3D models from one epoch to the other.     

结合边界约束网络和分水岭分割算法的建筑物提取

城市作为高密度建筑区域,在较小范围内有大量结构相似的建筑紧密分布。当前从高分辨率图像中准确检测建筑仍然是一个挑战,本文受边缘检测网络启发,提出一种强化边界精度的建筑物提取新方案,根据建筑物及边界特点改进深度网络,结合自下而上分组的分水岭分割提高分类精度和建筑边界的准确度。首先对数据预处理,生成建筑边界和建筑分割线两类辅助标签;改进性能较优的建筑检测框架ICT-Net网络,修改网络结构和损失函数,针对两类辅助标签,强化边界影响,提高网络性能;最后对网络预测结果应用结合分水岭分割和梯度提升回归树的后处理,实现高精度的建筑提取。结果表明,数据预处理、改进深度学习算法可提高建筑检测像素精度IOU（Intersection over Union）约1%。后处理能充分利用网络输出的概率信息,有效优化建筑边界,在网络预测结果的基础上提高建筑实例召回率10.5%。本文方案与原始的ICT-Net网络相比,提高建筑实例召回率22.9%。     

A Multiscale Object-Specific Approach to Digital Change Detection

Landscape spatial pattern is dependent not only on interacting physiographic and physiological processes, but also on the temporal and spatial scales at which the resulting patterns are assessed. To detect significant spatial changes occurring through space and time three fundamental components are required. First, a multiscale dataset must be generated. Second, a change detection framework must be applied to the multiscale dataset. Third, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce an object-specific multiscale digital change detection approach. This approach incorporates multitemporal SPOT Panchromatic (Pan) data, object-specific analysis (OSA), object-specific up-scaling (OSU), marker-controlled watershed segmentation (MCS) and image differencing change detection. By applying this framework to SPOT Pan data, image-objects that have changed between registration dates can be identified and delineated at their characteristic scale of expression. Results illustrate that this approach has the ability to automatically detect changes at multiple scales as well as suppress sensor related noise. This study was conducted in the forest region of the Örebro Administrative Province, Sweden.     

结合像元级和目标级的高分辨率遥感影像建筑物变化检测

快速、精准的建筑物变化检测对城市规划建设等业务管理具有重要意义。随着卫星遥感技术的快速发展,基于高分辨率遥感影像的建筑物变化检测得到了广泛关注。针对像元级建筑物变化检测方法往往精度不足而目标级建筑物变化检测方法过程烦琐等问题,本文提出结合像元级和目标级的高分辨率遥感影像建筑物变化检测方法。首先综合高分辨率遥感影像的多维特征,利用随机森林分类器进行影像集分类,以获取像元级建筑物变化检测结果;然后对后时相遥感影像进行图像分割,获得影像对象;最后融合像元级建筑物变化检测结果和影像对象,识别变化的建筑物目标。利用双时相QuickBird高分辨率遥感影像进行建筑物变化检测试验,结果表明：本文提出的方法能够削弱光照、观测角度等环境差异对建筑物变化检测的影响,显著改善建筑物变化的检测精度。     

高分辨率遥感影像场景变化检测的相似度方法北大核心CSCD

针对传统高分辨率遥感影像场景变化检测流程复杂且严重依赖分类结果的问题,本文提出了一种顾及场景全局与局部相似性的变化检测方法。首先,将同一区域两个时相的遥感影像裁切成固定尺寸的图像块,构造场景对图像库,并划分为训练集和测试集;其次,构建融合场景全局与局部相似性的双分支卷积神经网络,实现场景相似度学习;然后,利用训练的相似度学习网络提取训练集场景相似度,并通过阈值遍历的方法得到最佳的相似度阈值;最后,基于相似度阈值将测试集场景对划分为变化场景和未变化场景,得到最终的变化检测结果。试验结果表明,本文方法的总体精度为0.94,Kappa系数为0.88,优于传统的分类后变化检测方法,是一种简单有效的场景变化检测方法。     

一种基于影像分割的城市建筑物变化检测方法

针对高分辨率遥感影像建筑物变化检测精度不高的问题,本文提出了一种改进城市建筑物变化检测方法。首先通过提取像元顶点构造像元图集,并以长宽比与矩形度作为变化检测测度,对后一期影像进行影像分割,识别建筑物轮廓对象。将建筑物轮廓进行几何关系筛选,完成建筑物变化信息提取。实验表明,该方法具有较高的变化检测精度,可明显削弱光照条件和成像角度对建筑物变化检测精度的影响,是一种普适性较强的城市建筑物变化检测方法。     

基于生成对抗网络的建筑物损毁检测EI北大核心CSCD

建筑物作为自然灾害中最受影响的承灾体之一,其损毁信息的准确提取对灾后应急救援具有十分重要的意义。本文借鉴多模态的思想,提出了一种自动检测损毁建筑物的recursive-generative adversarial networks(RS-GAN)方法,将损毁建筑物检测分为灾前建筑物识别和灾后损毁建筑物检测两个任务,且分别在两个GAN分支中完成。RS-GAN加入联合损失函数将两个GAN分支进行连接,充分利用两个任务之间的潜在互利性提升检测效果。RS-GAN利用第1条GAN分支识别建筑物灾前形状与位置,并将识别结果作为第2条GAN分支的输入进行损毁建筑物检测任务,从而使检测结果具有更清晰的轮廓。该方法为端到端模型,在不需要过多的人工干预情形下,实现了损毁建筑物的自动检测。为了验证RS-GAN模型的效果,在圣罗莎和密苏里两个数据集上进行了测试。试验结果表明,RS-GAN方法拥有更好的检测性能,在圣罗莎数据集上的总体精度和平均精度分别达到了0.90和0.86。     

Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge

The updating of geodatabases (GDB) in urban environments is a difficult and expensive task. It may be facilitated by an automatic change detection method. Several methods have been developed for medium and low spatial resolution images. This study proposes a new method for change detection of buildings in urban environments from very high spatial resolution images (VHSR) and using existing digital cartographic data. The proposed methodology is composed of several stages. The existing knowledge on the buildings and the other urban objects are first modelled and saved in a knowledge base. Some change detection rules are defined at this stage. Then, the image is segmented. The parameters of segmentation are computed thanks to the integration between the image and the geodatabase. Thereafter, the segmented image is analyzed using the knowledge base to localize the segments where the change of building is likely to occur. The change detection rules are then applied on these segments to identify the segments that represent the changes of buildings. These changes represent the updates of buildings to be added to the geodatabase. The data used in this research concern the city of Sherbrooke (Quebec, Canada) and the city of Rabat (Morocco). For Sherbrooke, we used an Ikonos image acquired in October 2006 and a GDB at the scale of 1:20,000. For Rabat, a QuickBird image acquired in August 2006 has been used with a GDB at the scale of 1:10,000. The rate of good detection is 90%. The proposed method presents some limitations on the detection of the exact contours of the buildings. It could be improved by including a shape post-analysis of detected buildings. The proposed method could be integrated into a cartographic update process or as a method for the quality assessment of a geodatabase. It could be also be used to identify illegal building work or to monitor urban growth.