Building population mapping with aerial imagery and GIS data

Geospatial distribution of population at a scale of individual buildings is needed for analysis of people's interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA.     

DEM融合算法及其在困难地区测图中的应用

对于我国西部高山区,如横断山脉,高程起伏明显,常年被云雾覆盖,日照稀少,采用传统方法进行地形图测绘存在较大困难,依赖单一方法获取的DEM往往难以满足测图的精度要求。为充分利用不同传感器和不同方法生成的DEM的优点,本文根据各方法的特点,结合小比例尺地形图中低精度的DEM,基于绝对精度等先验知识确定优先级别、相关/干系数确定融合权重,提出了一种包括雷达干涉测量、光学立体摄影测量、不同侧视方向像对雷达立体测图生成的四种多源DEM的像素级融合算法。在横断山脉地区使用所提融合算法进行了实验,获得了一个总体精度得到提高的无缝DEM,实验结果表明新算法为地形复杂的测图困难地区DEM获取提供了一种可能的解决方案。     

基于GIS和神经网络的森林植被分类

本文综述了国际遥感分类研究,使用Landsat7 ETM＋遥感数据和地理辅助数据,应用BP神经网络方法,将莽汉山林场作为研究区进行了遥感影像的分类研究。比较了BP神经网络分类与最大似然、简单和复杂非监督分类法之间的类型与数量精度。BP神经网络分类的总类型精度是70.5%,总数量精度为84.65%,KAPPA系数是0.6455。结果说明BP神经网络的分类质量优于其他方法,其总的类型精度与其他三种分类方法相比分别增加了10.5%、32%和33%,总的质量精度增加了5.3%。因此,辅以地理参考数据的BP神经网络分类可以作为一种有效的分类方法。     

创新创业教育与专业教育相融合的测绘工程专业人才培养模式探讨

以培养符合社会需求的人才为目标,将创新创业教育与人才培养体系、专业课程体系、专业课程体系、教学和实践平台深度融合,从而构建了多学科交叉、整合优化教学和实践资源,集业务素养、创新创业教育、产学研结合等为一体的高度融合的人才培养体系,培养具备较强的创新创业能力的应用型测绘类人才。     

浅谈如何提高测绘工程专业学生对遥感原理与方法课程学习的兴趣

结合笔者在中南大学为期9年的遥感原理与方法课程教学实践,分析了中南大学测绘工程专业学生在该课程教学方面的学习态度和课程认识方面的现状,提出在教学过程初期加强学生对遥感历史文化及名人传记的学习,在课程教学期间自己查询专业应用前景及专业最新动态新闻,在上课期间主动向老师提问3种方法,以考核方式计入学生平时成绩,以期增强学生对该课程的学习兴趣和主动学习能力及独立思考能力,为后续遥感专业相关课程的学习奠定一定的基础。在近几年的教学实践过程中,发现该方法有效地提高了学生对该专业课的学习兴趣,学习效果超越往届学生,实践结果充分证明了该方法的有效性。     

SPOT4-VEGETATION中国西北地区土地覆盖制图与验证

利用SPOT4 VEGETATION的遥感数据产品生成的NDVI与NDWI植被指数时间序列图像集 ,通过ISODATA非监督分类方法 ,编制中国西北地区土地覆盖图。以TM影像人工解译结果作为真实值 ,通过对西北五省共计 47个均匀分布且异质性强的 2 5km× 2 5km样本区内的土地覆盖类型及其面积进行统计分析 ,修正了SPOT4 VEGETATION的土地覆盖分类系统 ,建立了各省验证结果的样本统计直方图并计算其回归系数。结果表明SPOT4 VEGETATION中国西北地区土地覆盖图在总体上具有较高的准确性。影响遥感数据自动分类精度 ,造成土地覆盖误判的原因主要来源于两个方面 :即异物同谱和混合像元问题。对于前者通过叠加各种辅助数据如DEM等可以降低误判的机率 ;对于后者运用混合像元分解的各种算法可以提高分类精度     

深度学习在遥感影像分类与识别中的研究进展综述

深度学习一直是机器学习和人工智能研究的热门主题,特别是将深度学习这一深层网络学习算法和遥感影像分类与识别联合起来,使得传统训练算法的局部最小性得以解决。本文首先简要介绍了遥感影像分类与识别算法的发展和经典算法的局限性,其次介绍了深度学习的几种主流算法并分析它们在遥感影像分类与识别处理方面的应用现状,最后对未来深度学习应用于遥感识别与分类趋势进行了展望。     

商业高分辨率遥感卫星及其在测绘中的应用

首先介绍了光学遥感卫星成像的工作原理,重点介绍了传输型光学遥感卫星中CCD相机的工作原理,卫星成像过程,卫星立体成像原理和遥感卫星的一些重要参数。文章第二部分介绍了当前正在运行的几种高分辨率商用光学遥感成像卫星,以及它们在商业上的应用。     

结合对抗网络与辅助任务的遥感影像无监督域适应方法

使用机器学习进行遥感影像标注的一个重要前提是有足够的训练样本,而样本的标注是非常耗时的。本文采用了域适应的方法来解决遥感影像场景分类中小样本量的无监督学习问题,提出了结合对抗网络与辅助任务的遥感影像域适应方法。首先建立了基于深度卷积神经网络的遥感影像分类框架;其次,为了学习到域不变特征,在标签分类器的基础上增加域分类器,并使域损失函数在其反射传播时的梯度与标签损失的梯度相反,从而保证域分类器不能区分样本来自于哪个域;最后引入了辅助分类任务,扩充了样本的同时使网络更具泛化能力。试验结果表明,本文方法优于主流的无监督域适应方法,在小样本遥感影像无监督分类中得到了较好的效果。     

机载LiDAR技术支持下的荒漠地区1:10 000地形图测绘与研究——以内蒙古阿拉善地区为例

尝试应用机载LiDAR技术测绘1：10 000比例尺地形图3D（DLG、DEM、DOM）产品,给出了机载LiDAR测绘3D产品的技术流程,并选择荒漠地区作为试验区,验证了此种技术方法在荒漠地区测绘3D产品的可行性,分析了成果精度。试验证明,该方法可以满足荒漠区域的1：10 000比例尺3D基础数据生产要求,且具有外业工作量小、自动化程度高、成图快、高程精度高、受外界环境影响小等优点,同时也总结了该方法中有待完善之处。该方法为荒漠地区3D基础测绘数据获取提供了有益借鉴。     

Identifying a suitable combination of classification technique and bandwidth(s) for burned area mapping in tallgrass prairie with MODIS imagery

Prescribed fire is crucial to the ecology and maintenance of tallgrass prairie, and its application affects a variety of human and natural systems. Consequently, maps showing the location and extent of these fires are critical to managing tallgrass prairies in a manner that balances the needs of all stakeholders. Satellite-based optical remote sensing can provide the necessary input for this mapping, but it requires the development mapping methods that are specific to tallgrass prairie. In this research, we devise and test a suitable mapping method by comparing the efficacy of seven combinations of bands and indices from the MODIS sensor using both pixel and object-based classification methods. Due to the relatively small size of many prescribed fires in tallgrass prairie, scenarios based on the 250 m spatial resolution red and NIR bands outperformed those based on the coarser 500 m spatial resolution bands, and a combination of both red and NIR performed better than each 250 m band individually. Object-based classification offered no improvement over pixel-based classification, and performed poorer in some cases. Our results suggest that mapping burned areas in tallgrass prairie should be done at a minimum of 250 m spatial resolution, should used a pixel-based classification technique, and should use a combination of red and NIR.     

Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region

In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting.