测绘用离轴三反光学系统技术

离轴三反光学系统可以同时兼顾长焦距与大视场, 可以优化为零畸变、低场曲的光学系统, 很好地满足了测绘对光学系统的要求, 被公认为航天遥感测绘相机的发展方向。本文阐述了航天测绘相机的现状和发展趋势, 对离轴三反光学系统应用于测绘的相机内方位元素定义、焦距计算公式的修正、调焦方式对主点位置精度的影响、系统畸变标定以及系统稳定性等问题进行讨论为中国自主获取高分辨率、高精度的测绘数据提供了技术参考。     

“天绘一号”卫星测绘相机在轨几何定标

卫星在空间环境运转过程中, 测绘相机的几何参数会发生不可预估的变化, 从而对摄影测量定位精度和数据处理精度产生影响。因此, 所有测绘卫星都对星载相机的几何参数进行在轨定标, 监测相机几何参数在轨运行状态下的变化情况。本文着重介绍了“天绘一号”卫星在轨几何定标的内容和采用的方法。     

测绘卫星的姿态控制技术

通过分析测绘卫星的姿态控制要求, 研究了卫星姿态控制所涉及的主要误差源, 提出了相应的解决措施, 针对测绘卫星的需求进行了姿态控制系统方案设计。     

时间序列InSAR技术中的形变模型研究

时间序列InSAR技术,包括PS-InSAR技术和小基线集InSAR技术, 能有效克服传统D-InSAR的失相干限制, 逐步成为形变测量中的实用化技术。但是, 现有时间序列InSAR技术主要采用线性函数对真实形变进行模拟, 在真实形变呈现强烈的非线性时, 这种处理将不能得到正确的形变结果。本文就时间序列InSAR的形变模型问题展开研究, 首先从干涉相位模型解算的方法入手, 深入分析了线性形变模型的不足, 当干涉点目标的密度不够并且真实形变的非线性较强时, 干涉相位方程的解将会发散。根据魏尔斯特拉斯逼近定理, 提出以高阶多项式取代线性形变模型, 并给出了基于多项式形变模型的干涉相位方程解算方法。 利用太原市2003-2009年的23景ENVISAT ASAR影像, 分别采用线性形变模型和三次多项式形变模型, 利用小基线集技术进行了形变反演。将这两种方法得到的结果分别与水准测量结果进行了比较。结果表明, 采用多项式形变模型不仅能取得更高的形变测量精度,而且能提高点目标的密度。由于高阶多项式总能比低阶多项式更准确地拟合连续函数, 因此本文提出的多项式形变模型在时间序列InSAR形变监测中具有广泛的应用价值。     

巷道地质编录三维空间数字化及投影变换算法

本文以提高矿山地、测、采计算机管理与辅助设计质量和效率为基本目标,根据矿山地测数据处理的一般流程,从巷道地质编录图件出发,建立基于井下测量数据的巷道地质编录三维空间数字化及投影变换算法;在此基础上,研制了一套符合矿山生产实际的地质编录智能分析系统,实现了巷道地质编录图件的数字化及自动成图。     

监测地理国情对测绘发展的引领与拓新

开展地理国情监测,国家测绘地理信息局已经有了很好的成果资料积累、技术基础和人才梯队,但是要形成一个完整的地理国情监测体系还需要科技创新和实践探索。目前主要任务是：充分利用已取得的基础测绘成果及其与地理国情监测相关方面探索出的成功经验,准确把握地理国情监测的重点和难点,创新思维,勇于探索,破解难题,为全面开展地理国情监测工作铺平道路。     

浅谈基于粗纠正影像源进行1∶10000 DLG生产“内外业一体化”工艺流程

为了减少外业工作量,利用航空摄影测量进行基础地理信息采集是最有效手段之一,国内外推出了一系列数字摄影测量的软件和系统.本文结合GEOWAY DPS/IS及VirtuoZo AAT(PATB)两种软件的生产实践,探讨了基于粗纠正影像源进行1∶10000数字线划图(DLG)生产“内外业一体化”的工艺流程.本文对以后采用此种方法成图具有一定的指导作用.     

构建信息化测绘保障体系的思考

结合工作实际,探讨了信息化测绘保障体系的基本内涵、主要特征,以及构建信息化测绘保障体系应强化的四种信息意识和应把握的四个重点问题,以供商榷。     

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.     

An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery

We tested the effects of three fast pansharpening methods – Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Additive Wavelet Transform (AWT) – on sugarcane classification in a Landsat 8 image (bands 1–7), and proposed two ensemble pansharpening approaches (band stacking and band averaging) which combine the pixel-level information of multiple pansharpened images for classification. To test the proposed ensemble pansharpening approaches, we classified “sugarcane” and “other” land cover in the unsharpened Landsat multispectral image, the individual pansharpened images, and the band-stacked and band-averaged ensemble images using Support Vector Machines (SVM), and assessed the classification accuracy of each image. Of the individual pansharpened images, the AWT image achieved higher classification accuracy than the unsharpened image, while the IHS and BT images did not. The band-stacked ensemble images achieved higher classification accuracies than the unsharpened and individual pansharpened images, with the IHS-BT-AWT band-stacked image producing the most accurate classification result, followed by the IHS-BT band-stacked image. The ensemble images containing averaged pixel values from multiple pansharpened images achieved lower classification accuracies than the band-stacked ensemble images, but most still had higher accuracies than the unsharpened and individual pansharpened results. Our results indicate that ensemble pansharpening approaches have the potential to increase classification accuracy, at least for relatively simple classification tasks. Based on the results of the study, we recommend further investigation of ensemble pansharpening for image analysis (e.g. classification and regression tasks) in agricultural and non-agricultural environments.