空-谱融合的条件随机场高光谱影像分类方法

高光谱遥感数据具有光谱信息丰富、图谱合一的特点,目前已经广泛地应用在对地观测中。传统的高光谱分类模型大多过分依赖影像光谱信息,没有充分利用空间特征信息,这使得分类精度还有很大的提升空间。条件随机场是一种概率模型,能够较好地融合空间上下文信息,在高光谱影像分类中已经得到越来越多的关注,但大部分条件随机场模型存在超平滑的现象,会导致影像细节丢失。针对该问题,本文提出了一种优化融合影像空-谱信息的高分辨率/高光谱影像分类方法,该方法将影像的纹理信息与原始光谱信息进行融合,利用SVM分类器对其进行预分类,并将各类概率定义为一元势函数,以融合空间特征信息;然后将空间平滑项和局部类别标签成本项加入二元势函数中,以考虑空间背景信息,并保留各类别中的详细信息。最后,通过两组的高分辨率/高光谱影像数据进行试验。结果表明,与SVM算法、传统的条件随机场方法和面向对象的分类方法相比,本文提出的算法在整体分类精度上分别提高了10%、9%和8%以上,同时在保持地物边缘完整性、避免“同谱异物”与“同物异谱”的现象方面有较明显的优势。     

基于深度学习理论的无人机影像分类

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。     

Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city

ABSTRACT

Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability.     

Satellite image analysis using crowdsourcing data for collaborative mapping: current and opportunities

ABSTRACT

Researchers are continually finding new applications of satellite images because of the growing number of high-resolution images with wide spatial coverage. However, the cost of these images is sometimes high, and their temporal resolution is relatively coarse. Crowdsourcing is an increasingly common source of data that takes advantage of local stakeholder knowledge and that provides a higher frequency of data. The complementarity of these two data sources suggests there is great potential for mutually beneficial integration. Unfortunately, there are still important gaps in crowdsourced satellite image analysis by means of crowdsourcing in areas such as land cover classification and emergency management. In this paper, we summarize recent efforts, and discuss the challenges and prospects of satellite image analysis for geospatial applications using crowdsourcing. Crowdsourcing can be used to improve satellite image analysis and satellite images can be used to organize crowdsourced efforts for collaborative mapping.     

陆源碎屑岩中的古土壤在地层分析中的应用

对各种古土壤的分类标准进行比较之后,重点介绍了Mack的古土壤分类及其土壤的演变序列。Mack的古土壤分类主要依据稳定矿物和保存在土壤中的古地貌属性,将古土壤划分为9种。在地层序列中古土壤的识别标志主要包括颜色、粒度、生物成因构造（植物生长痕迹）、原生沉积构造退化特征、层次化和边界特征、宏观结构特征、矿物及地球化学特征等。可以通过野外观察、室内显微镜观察、实验室化验及地震、测井资料来进行鉴别。陆相盆地具有很强的分隔性,盆地相变又非常复杂,寻找区域性较稳定的层序界面比较困难。越来越多的研究证实用古土壤划分陆相成因地层层序、解决地区及全球对比具有重要的实用价值。     

遥感技术在黑竹沟风景旅游资源调查分类与评价中的应用

随着信息技术的发展,遥感成为了旅游资源调查评价的新手段。阐述了运用遥感新技术对四川省乐山市黑竹沟风景旅游区旅游资源进行调查的过程,并结合现场调研和相关资料对其旅游资源进行了分类和评价。首先对黑竹沟风景旅游区2003年的ETM影像数据遥感图像进行了处理,然后建立了解译标志进行旅游资源信息的提取和分析,最后对黑竹沟风景旅游区的旅游资源进行了客观全面的分类和评价。黑竹沟的旅游资源可分为2大类、7亚类、21个基本类型,是一处景源内容丰富、景象多变的旅游区,发展潜力巨大。     

全球及区域模式中陆面过程的地表植被覆盖分类方法

全球和区域尺度上陆面生态系统与气候密切相关。全球和区域模式的发展对于我们认识气候与陆地生态的变化起到重要作用。在这些模型中,植被覆盖是影响大气-植被间热量、水分和CO2等交换的重要陆面参数。在分析了陆地植被覆盖分类原理基础上,介绍了目前全球不同植被覆盖分类方案,包括基于地基观测的植被分布、基于生物气候特征的分类方案（如：Holdridge的方案）;特别近年来陆面过程试验表明,各种遥感数据源（如：NOAA—AVHRR,EOS—MODIS,Landsat—TM）等为我们提供了有利的工具来监测全球植被动态,完善植被分类,并且采用高时空分辨率的全球土地覆盖状况特征,在不同时空尺度揭示植被-大气相互作用。本文分析了代表性的3种基于卫星遥感技术的陆面植被分类方案,分别是BATS（18类）、SIB（9类）、SIB2（12类）和BIOME—BGC（31类）陆地模式的植被覆盖分类方案．最后分析了目前可用于全球植被覆盖分类的新的遥感数据库。     

页岩岩相分类表征及对建产区产能的影响

单井产气能力差异大,导致持续稳产困难是页岩气建产区面临的关键难题.围绕页岩气建产区特点,建立了考虑建产区产气能力影响的优质海相页岩岩相分类表征技术流程.首先,分析了建产区优质页岩产气能力差异主控因素,制定了建产区优质页岩岩相分类方案;然后,建立了TOC含量,孔隙度,泥质、硅质、钙质矿物含量等关键参数测井计算模型,结合页...     

天津市地面沉降区地下水开采指标综合评定方法研究

实行地面沉降控制和地下水资源的统一管理,是天津市地面沉降防治工作的特色。本文针对地下水超采是影响天津市地面沉降主要因素的现状,通过对多年地面沉降监测以及地下水资源开发利用数据的分析,首先在全市范围内初步确定各乡镇2007年地下水开采指标或维持2005年实际开采水平,或选用一元回归分析方法建立地面沉降预测模型。在建模过程中,应用地质系统科学理论,综合考虑2001～2005年各年度地面沉降和地下水水位动态,将天津市划分为若干个子系统,每一子系统作为一个回归分析单元。为了将各计算单元的预测结果具体到各乡镇地下水资源开采指标制定中,结合各乡镇地面沉降现状和地下水替换水源条件,并根据计算单元内各乡镇地下水开采强度、地下水超采程度、沉降敏感度以及地下水位超限程度等因素进行压采规模的综合分级。这一方法已得到我市相关水资源管理部门的认同,并取得了较好的实践效果。     

地下管线普查产品的检验

随着城市地下管线在城市管理、规划及建设发展过程中重要性的逐渐体现,越来越多的城市开展了地下管线普查,但其质量检验与质量评定方法却相对滞后,本文结合实例阐述了地下管线普查产品的检验及质量评定方法,供大家参考。