面向对象的卫星影像积雪信息提取

针对传统基于像元的积雪提取方法的不足,该文以HJ-1B/CCD、IRS遥感影像作为数据源,通过分析HJ-1B影像上的地物特征,提出了先对影像特征合并后进行多尺度分割的积雪提取方法。多尺度分割时,首先将影像分割成多边形对象,然后建立起积雪提取的规则集,进而对积雪信息进行提取。为了验证提取精度,该文对比分析了NDSI方法和本文方法提取积雪的结果,并利用目视解译积雪提取结果定量地进行了精度评价。结果表明:采用本文方法提取积雪具有较高的可靠性和准确性。     

出租车轨迹数据快速提取道路骨架线方法

文章就如何利用出租车GPS轨迹数据提取道路信息展开研究:首先运用角平分线算法生成轨迹线的缓冲区,再利用点水平切割中点线法从缓冲区中提取道路骨架线的方法。该方法快速、简单、实用,对大多数道路的结构情况都能够很好地进行描述。最后利用昆明市出租车数据进行实验,并将实验结果与影像数据对比,证明提取的道路骨架线与影像吻合较好。     

在高分辨率遥感影像中提取无清晰连续边缘线的道路

现有的许多道路提取算法均利用道路的外边缘线信息来实现道路的提取,当边缘线清晰连续时,采用这些方法都可以取得很好的提取效果.不过,在高分辨率的城市遥感影像中,常常会存在一些低对比度区域,处于其中的道路边缘线非常之弱,以致难以直接检测出单个的边缘点.如果受到树木、房屋及车辆的干扰,这些原本就很弱的边缘还会发生断裂.通过现有方法提取具有如此边缘线的道路难度很大.本文给出一种旨在解决这一问题的新方法.首先借鉴相位编组原理形成边缘线支持区并对其进行连接;然后利用动态规划方法从支持区中检测出边缘线并对这些线进行平滑;最后连接由边缘线构成的道路段,得出道路提取结果.实验表明,本方法可以较好地提取出无清晰连续边缘线的道路,对于边缘对比度较大的道路则可取得更为令人满意的结果.     

基于车载LiDAR的交通标线自动提取方法研究

针对自动驾驶技术对高精度道路信息实时存储分析的需求日渐增大、道路点云数据冗余离散的问题,本文提出了一种从车载LiDAR点云数据中自动提取道路面、分类并矢量化交通标线的有效方法。首先,将点云数据中的非地面点滤除;其次,基于载体车辆的行车轨迹线生成伪扫描线实现道路面的提取;然后,构建一系列二维点云参考影像,利用点云强度等特征信息检测交通标线边界像素点及坐标,并去除离群值对交通标线进行分类细化;最后,对本文方法提取与传统方法提取的交通要素进行对比,实验结果表明,本文提取方法的准确度及效率都有了一定的提升。     

机载LiDAR和高光谱数据融合提取冰川雪线

以西藏那曲县境内的“中习一号”冰川为研究区,对2011年8月获取的机载激光雷达点云进行预处理和滤波分类,提取研究区数字高程模型(digital elevation model,DEM);将DEM数据分别与同期获取的机载高光谱栅格数据和提取出的冰川矢量数据进行三维地形模拟,利用DEM数据对高光谱最大似然法分类结果进行正射纠正,从而获取研究区的数字正射影像(digital orthophoto map,DOM);最后结合研究区DOM和机载点云数据提取“中习一号”冰川的雪线.结果表明:融合机载高光谱和机载激光雷达2种数据的优势,能更方便地提取出冰川雪线,而且能很好地显示雪线的高度.     

基于霍夫直线检测和区域生长的合成孔径声呐图像线目标提取

本文针对合成孔径声呐图像线目标提取过程中,由于线目标宽度（直径）小且图像受相干斑噪声影响严重而导致的提取困难的问题,提出了基于霍夫直线检测和区域生长的线目标提取算法。首先利用Otsu算法计算区域生长终止阈值,并对噪声进行初步抑制;然后在对图像进行霍夫直线粗检测的基础上,对检测结果进行不均匀分割,再进行细检测,得到区域生长种子点;最后利用区域生长对线目标进行还原。试验结果表明,该算法能准确地提取线目标;与传统区域生长对比,该算法选取的种子点数量提升了5.37倍,且线目标提取结果具有更好的连续性和完整性。     

结合遥感影像和DEM的线性体特征提取

利用遥感影像提取的线性特征具有较高定位精度,但不能保证线性特征和地质构造相关;利用DEM提取的线性特征能较好地反映地形特征,但存在边缘点定位不准、难以连接的缺点。本文提出一种结合两者优势的线性体特征提取方法。首先利用嵌入边缘的标记分水岭分割方法提取遥感影像线性特征,在保留弱边缘的同时去除伪边缘;然后利用DEM地形和水文分析提取断裂带、山脊线、山谷线;最后利用边缘点匹配保留与地质构造相关的边缘点利用数学形态学的腐蚀、膨胀方法进行结果后处理,连接相邻边缘点以获得最终结果。秦皇岛市山区的试验结果表明:该方法能快速、准确地获得连续线性体特征,较好地刻画研究区的地质构造情况。     

基于图像分块和线段投票的遥感道路边缘线提取

从遥感图像中提取边缘线是一个经典的课题,不同的边缘提取算法适用于不同类型的图像。实际图像中道路的几何形状不甚规则,因受到建筑和树木遮挡导致对比度较低且噪声影响较严重,道路边缘线会发生断裂,故高分辨率遥感图像道路边缘线提取一直是一个研究热点。针对现有方法很难提取出清晰连续的道路边缘线问题,提出一种遥感图像道路边缘线提取新方法:首先通过方向模板检测边缘点,搜索出分块图像中的子线段;然后延伸子线段并进行投票,连接处于弯曲边缘线的直线段,将长度大于特定阈值的边缘线作为输出结果;最后去除毛刺和分叉,取8个方向道路的并集作为最终道路网。实验结果表明,该方法能够从高分辨率遥感图像中较好地提取带有一定曲率、对比度较低、噪声影响严重的道路边缘线。     

车载LiDAR点云铁路横断面及轨顶点提取方法研究

为了更加高效地对铁路空间信息数据进行复测,研究利用车载LiDAR系统扫描获取铁路场景中的点云数据,提取铁路横断面轨顶点的方法,该文提出了一种基于POS线投影的铁路横断面轮廓及轨顶点提取算法,该方法以POS线为辅助线对铁路横断面点云进行切割,采用Alpha Shapes算法提取铁路横断面轮廓点云数据,并结合铁路横断面以及POS线的空间特性,实现了铁路横断面轨顶点的提取.实验及分析评价结果表明,基于POS线投影的铁路横断面及轨顶点点云提取算法是有效且准确的,对其他各类铁路地物信息的提取具有一定的参考和借鉴价值.     

从卫星遥感资料中提取雪盖信息的探讨

根据卫星遥感影像和数据资料,通过目视判读和数字化图像处理,摸索如何提取雪盖面积、雪线高度和积雪消融等信息及雪盖分类与制图方法,并对相应的方法进行了评价。 对于云与雪的区别问题,利用不同的资料及处理方法对其可分性进行了分析,发现Landsat TM资料中5波段(1.55─1.75μm)和7波段(2.08─2.35μm)以及4(0.78─0.90μm),5,7波段合成对于云与雪的区别效果较好。     

青藏高原冰雪覆盖变化监测的卫星立体影像方法

针对青藏高原冰雪覆盖变化监测问题,以各拉丹冬冰川为例,提出了利用ALOS立体像对提取冰雪DEM,并以同源不同时相的冰雪DEM监测冰雪覆盖变化及体量变化的方法。依据DEM纠正裁切后的影像,采用ISODATA分类方法得到了冰雪覆盖面积变化量,由不同时相网格DEM高程点数据高差与面积求得体积变化,得出了2009年与2010年冬季冰雪量大小。结果显示2010年12月比2009年12月冰雪量减少了19.728 3 km³,雪覆盖面积减少了349.691 km²。文中还列出了遇到的技术困难及有待进一步研究的问题。     

USE OF SATELLITE IMAGERY IN ESTIMATING THE MOISTURE CONDITION OF SNOW COVER

A method for determining areas of most intensive melting within a mountain snowpack is described, based on an inverse relationship between reflectance and age of snow at wavelengths greater than 0.8 μm. Comparisons between the area of snow cover detected on visible and near infrared satellite imagery at different times during the spring thaw, and more specifically differences in position of the snowline, permit identification of the upper boundary of a zone of intensive snow-melt and water discharge from the snow cover. Translated from: Distantsionnyye Issledovaniya rel'yefa Sibiri, A. L. Yanshin and V. N. Sharapov, eds. Novosibirsk: Nauka, 1985, pp. 88-91.     

SAR interferometric coherence analysis for snow cover mapping in the western Himalayan region

Abstract

Information of snow cover (SC) over Himalayan regions is very important for regional climatological and hydrological studies. Precise monitoring of SC in the Himalayan region is essential for water supply to hydropower stations, irrigation requirements, and flood forecasting. Microwave remote sensing has all weather, day and night earth observation capability unlike optical remote sensing. In this study, spaceborne synthetic aperture radar interferometric (InSAR) coherence analysis is used to monitor SC over Himalayan rugged terrain. The feasibility of monitoring SC using synthetic aperture radar (SAR) interferometry depends on the ability to maintain coherence over InSAR pair acquisition time interval. ERS-1/2 InSAR coherence and ENVISAT ASAR InSAR coherence images are analyzed for SC mapping. Data sets of winter and of snow free months of the Himalayan region are taken for interferogram generation. Coherence images of the available data sets show maximum decorrelation in most of the area which indicates massive snowfall in the region in the winter season and melting in the summer. Area showing coherence loss due to decorrelation is mapped as a snow-covered area. The result is validated with field observations of snow depth and it is found that standing snow is inversely related to coherence in the Himalayan region.     

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km² river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.     

Snow and forest cover assessment of uttaranachal state using irs 1c wifs data

In this study, IRS 1C WiFS data have been used for the assessment of two natural resources i.e. forest cover and snow cover. These two resources have a great role to play in various hydrological studies such as floods, soil erosion and water pollution etc. Therefore their assessment is very useful in various hydrological studies and management of these resources. The assessment of snow and forest cover have been made on the basis of multispectral classification and classification of NDVI images. Newly created Uttaranchal state has been taken as the study area. These two resources have been estimated for all the thirteen districts of the state separately. The forest cover area estimated in this study is compared with the available data sets of Forest Survey of India (FSI). The estimated forest is 52%, whereas the forest cover reported by the FSI is 44.5% of the total geographical area of the state. The snow cover is estimated for the period after winter season i.e. maximum snow cover and before next winter season i.e. minimum snow cover. It is found that one quarter of the state is under snow cover covering six districts of the state. As such no estimate of snow cover at regional scale has been made so far therefore comparison of the present assessment could not be made.     

Monitoring of Snow Cover Variability in Chenab Basin Using IRS AWiFS Sensor

Monitoring of seasonal snow cover is important for many applications such as melt runoff estimation, climate change studies and strategic requirements. Contribution of seasonal snow melt runoff of Chenab River is significant and important to meet hydrological requirement at foothills. Seasonal snow cover of Chandra, Bhaga, Miyar, Bhut, Warwan and Ravi, six major tributaries of Chenab River, becomes crucial to assess the water availability. In addition, altitudinal distribution of snow cover significantly influences the melt runoff which is highly sensitive to minor variations in atmospheric temperature. In this investigation, remote sensing based Normalized Difference Snow Index technique has been used to generate 10 daily snow cover product. Snow cover monitoring of all the sub-basins were carried out for 10 years from 2004–2005 to 2013–2014 during hydrological year (October to June) using Advanced Wide Field Sensor (AWiFS) of Indian remote sensing satellite (IRS). Accumulation and ablation patterns of snow cover have also been analyzed for the six sub-basins. Accumulation and ablation pattern of snow cover, from 2004 to 2014 which shows slightly increasing trend for all the sub-basins. Meteorological data of Kelong at Bhaga sub-basin was also analysed. Average monthly snow line altitude was estimated for all the sub-basins using hypsographic curve. Chandra and Bhaga sub-basins are at higher altitude and Ravi sub-basin is at lower altitude. It was also observed that areal extent of snow reaches to lower altitude during last 5 years, particularly in Ravi sub-basin.     

Spatial and Temporal Variation Analysis of Snow Cover Using MODIS over Qinghai-Tibetan Plateau during 2003–2014

The Qinghai-Tibetan Plateau (QTP) snow cover information acquisition of the high precision spatial and temporal characteristics is of great significance for the research on its land surface atmosphere coupled system and global climate change effects. The Moderate Resolution Imaging Spectro-radiometer (MODIS) daily snow cover products (MOD10A1 and MYD10A1) have been widely used in long time series of spatial and temporal variation analysis, but they are limited to be used because of high cloud cover ratio. In this paper, a 7-day rolling combination algorithm was presented to eliminate cloud obscuration, and the whole cloud amount falls below 7 %. The ground station in situ measurements verify that the overall precision is more than 90 %. The presented algorithm guaranteed the same spatial resolution and temporal resolution, and has higher precision than products MOD10A1 and MYD10A1. The MODIS 7-day rolling combination snow cover datasets products were obtained between 2003 and 2014 in the QTP, and the snow cover area of spatial and temporal variation was analyzed. The change characteristics of snow cover duration was also studied combining with the Digital Elevation Model data. Results show that the snow cover area of the whole QTP has a slowly decreased trend, but increases in autumn. Thus, the snow cover proportion of annual periodic and unstable in different elevations has the highest correlation with area of the elevation.     

利用多光谱卫星遥感和深度学习方法进行青藏高原积雪判识

青藏高原积雪对全球气候变化十分重要,针对已有积雪遥感判识方法中普遍采用的可见光与红外光谱数据易受复杂地形与高海拔影响,导致青藏高原地区积雪判识精度较低的问题,提出了一种基于多光谱遥感与地理信息数据特征级融合的积雪遥感判识方法:以风云三号卫星可见光与红外多光谱遥感资料与多要素地理信息作为数据源,由地面实测雪深数据与现有积雪产品交叉筛选出样本标签,构建并训练基于层叠去噪自编码器(SDAE)的特征融合与分类网络,从而有效辨识青藏高原遥感图像中的云、积雪以及无雪地表。经地面实测雪深数据验证,该方法分类精度显著高于使用相同数据源的FY-3A/MULSS积雪产品,略高于国际主流积雪产品MOD10A1与MYD10A1,并且年均云覆盖率最低。试验结果表明该方法可有效地减少云层对积雪判识的干扰,提升分类精度。     

近10年新疆积雪面积时空变化研究

区域尺度积雪信息的时空监测对确定雪灾的影响范围及灾情等级划分具有重要意义。本文利用近10年的MODIS积雪产品,按月最大面积的规则合成;分析了新疆积雪覆盖面积的时空变化特征,结果表明:时间上,新疆积雪面积有减少的趋势。空间上,近10年新疆积雪季节内永久性积雪覆盖区域主要分布在阿勒泰山脉、天山北麓及沿昆仑山脉西南部。其中天山及阿尔泰山之间的河谷及盆地的草原积雪面积波动主导了新疆整体积雪总面积的波动。     

用气象卫星资料监测积雪

本文简要叙述了利用气象卫星资料进行积雪监测的可行性和复杂性;以改进的甚高分辨率扫描辐射仪(AVHRR)资料为例综述了遥感监测积雪的原理、方法和资料处理过程;分析了计算结果,并探讨了未来积雪监测的发展。