基于卫星遥感影像的1∶5000地形图修测探讨

地形图体现测量时的地形地貌,随着城市建设和自然风蚀的影响,其逐渐与实地现状不一致,故实施地形图修测很有必要。本文基于卫星遥感影像的地形图修测方法进行了具体试验,提出了具体思路和作业流程,对地形图快速修测方法进行了有益探索。     

Remote sensing of scattered Natura 2000 habitats using a one-class classifier

Mapping of habitats with relevance for nature conservation involves the identification of patches of target habitats in a complex mosaic of vegetation types not relevant for conservation planning. Limiting the necessary ground reference to a small sample of target habitats would greatly reduce and therefore support the field mapping effort. We thus aim to answer in this study the question: can semi-automated remote sensing methods help to map such patches without the need of ground references from sites not relevant for nature conservation? Approaches able to fulfill this task may help to improve the efficiency of large scale mapping and monitoring programs such as requested for the European Habitat Directive.In the present study, we used the maximum-entropy based classification approach Maxent to map four habitat types across a patchy landscape of 1000 km² near Munich, Germany. This task was conducted using the low number of 125 ground reference points only along with easily available multi-seasonal RapidEye satellite imagery. Encountered problems include the non-stationarity of habitat reflectance due to different phenological development across space, continuous transitions between the habitats and the need for improved methods for detailed validation.The result of the tested approach is a habitat map with an overall accuracy of 70%. The rather simple and affordable approach can thus be recommended for a first survey of previously unmapped areas, as a tool for identifying potential gaps in existing habitat inventories and as a first check for changes in the distribution of habitats.     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

地理国情监测在城市化发展中的应用研究——以汕头市为例

开展地理国情监测工作的目的是为了获取自然与人类社会经济活动引起的地表变化信息,为政府、企业和公众提供地理国情信息服务。城市化发展、变迁是地理国情监测的重要应用方面之一,本文以广东省汕头市为例,基于遥感技术（ RS）,利用多时相影像数据提取不同时期建成区变化信息,并有针对性地对比分析,直观反映了汕头市三十多年的城市化发展的特点和进展情况。     

地理国情普查外业调查技术要求

地理国情外业调查是地理国情普查工作十分重要的工作内容,是保证地理国情普查数据质量的关键环节。外业调查是对采集的地理国情要素和解译的地表覆盖分类成果以及内业无法定性的类型、边界和属性进行实地调查,同时采集遥感影像样本数据,为最终形成地理国情要素数据、地表覆盖分类数据成果和遥感影像解译样本数据库提供基础。     

高分辨率遥感影像滤波算法综述

高分辨率卫星影像空间分辨率高,地物结构纹理信息突出,常用于土地利用监测、自然灾害预报等领域,但其所含的背景噪声影响了影像识别和分析的有效性和可靠性,因此,选取合适的滤波方法消除各种噪声成为遥感影像处理的首要任务。在遥感技术发展的几十年中,研究者们针对各种噪声类型已发展了多种滤波方法。本文分析了高分辨率遥感影像噪声的特点,介绍了一些传统的滤波算法和近年来广泛应用的新型滤波方法,并深入探讨各种滤波器的性能及其优缺点,为今后选择合适的算法消除高分辨率遥感影像噪声提供参考,最后对遥感影像滤波方法的发展前景进行了展望。     

上海市无居民海岛普查方法与资源分析

通过利用高分辨率遥感影像辅助确定海岛岸线,补充说明了海岛的界定方法,介绍了开展上海市无居民海岛地名普查的工作流程,并分析了上海市无居民海岛的分布及开发利用现状。     

浅谈地理国情普查中的遥感解译具体实施方案

遥感解译数据库的建立为地表覆盖自动化操作提供了可参考的数据源,对指导地理国情和省情建设具有指导意义。     

FCM聚类算法协同Canny算子的遥感影像边缘检测方法

提出一种FCM聚类算法协同Canny算子的遥感影像边缘检测方法,算法采用中值滤波消除原始遥感影像中的非高斯噪声;采用FCM(Fuzzy C-mean)聚类算法将滤波处理后的遥感影像中的像素分为两类:边缘类像素和非边缘类像素,并得到聚类影像;最后采用Canny算子对聚类影像进行边缘检测得到遥感影像地物边缘信息。实验结果表明,文中提出的方法能有效消除遥感影像中的混合噪声并准确地检测出地物目标的边缘,是一种有效的遥感影像边缘检测方法。     

基于嵌入式开发的便携式水域测量系统

基于水下地形测量原理,通过设计全新的船体结构和双动力系统,加载船载的GPS RTK和测深仪,完成了测量船的基本制造装配。通过开发岸基控制模块自动导航软件及基于嵌入式的船载控制模块,实现了遥控测量船的水下地形数据采集和远程控制功能。