AN AUTOMATIC ALGORITHM ON ESTIMATING SUB-PIXEL SNOW COVER FROM MODIS

积雪覆盖(snow cover)是气候、水文和生态环境等研究领域中很重要的参数之一.与积雪覆盖二值技术不同,亚像元雪盖反演技术可以在给定卫星观测空间分辨率前提下进一步提高积雪覆盖面积的反演精度.“多端元光谱混合分析”方法反演亚像元积雪覆盖度具有物理意义明确、精度高等优势.但由于其需要通过端元选取、最小二乘法反演等计算,运行效率较低,难以满足大数据量计算.我们发展了一种改进“多端元光谱混合分析”方法反演MODIS亚像元积雪覆盖的算法.该算法通过对MOD09GA数据进行图像端元自动提取,并利用能够代表图像端元类的典型端元库进行“多端元光谱混合分析”反演亚像元积雪覆盖.我们在端元选取、多端元线性混合模型分解等方面进行了改进与发展,不仅保证了产品精度,同时提高了计算的时效性.     

基于线性光谱模型的混合像元分解高性能计算研究

混合像元分解是遥感技术向定量化、精细化发展的重要技术,是关系到地物精细分类的重要操作环节,而线性光谱模型确实是目前解决混合像元问题的有效策略.针对高光谱遥感影像数据量大,混合像元分解计算耗时长的问题,提出了一种基于CUDA的高光谱遥感端元投影向量法实现方法.在分析高光谱图像端元投影向量法串行算法的基础上,建立了在CUDA架构下以像元点为基准产生相应的进程数,每个进程负责一单位像元点的计算方式.实验结果表明,将该方法应用于实际的高光谱遥感影像的混合像元中,可极大地提高传统中央处理器(CPU)的运算效率.     

基于混合像元分解的火星南极矿物制图研究

对火星高光谱遥感数据进行混合像元分解,有助于获取像元内部火星表面矿物含量。端元光谱提取和光谱解混是混合像元分解的关键技术。以ORB0942_2轨道覆盖的火星南极地区作为研究区,应用纯净像元指数法（PPI）从影像中提取出端元光谱,并利用线性分解模型对影像中混合像元进行了分解,计算出其各端元组分的百分含量,获得了研究区水冰、石膏、钙镁橄榄石及紫苏辉石的相对含量分布图。     

西藏羊卓雍错湖面遥感监测模型及近期变化

羊卓雍错作为西藏高原三大圣湖之一和藏南重要的高原特色风景旅游景区, 其水域变化受到当地老百姓和各级政府部门的关注. 应用高分辨率陆地资源卫星等遥感数据可以方便、 准确地获取湖泊面积、 周长等信息, 但是由于Landsat 等高分辨率陆地资源卫星影像受到卫星重复周期和卫星过顶时多云天气的影响, 无法实现湖泊面积变化的常规业务化连续监测. 为此, 以Landsat 等高分辨率陆地资源卫星为主要遥感信息源, 结合羊卓雍错水位观测资料, 建立了羊卓雍错湖泊面积变化与水位波动之间的相关模型. 在此基础上, 利用该湖泊面积遥感监测模型, 结合近期水位观测资料分析了羊卓雍错湖面面积变化特征与趋势.     

基于深度特征的双极化SAR遥感图像岩性自动分类

基于像元基元、极化合成孔径雷达（Synthetic Aperture Radar,SAR）数据和传统机器学习算法的岩性分类方法,易受SAR图像固有斑点噪声影响,精度不高.为了降低噪声的影响,本研究以大尺度像元邻域为基元,用于表征地表地质体的遥感图像特征和岩性语义信息;采用高分三号双极化SAR数据进行极化分解构建3通道假彩色合成影像;然后采用深度卷积神经网络（Deep Convolutional Neural Network,DCNN）迁移学习的方法,提取有效的深度特征表示,分别实现5 m和15 m两种空间分辨率下岩性遥感自动分类.结果表明:基于不同分辨率数据和不同DCNN算法,岩性遥感自动分类的总精度均大于80%,最高精度达到91%.基于大尺度像元邻域和DCNN迁移学习方法,能够实现基于SAR数据的高精度岩性分类.      

河流水体悬浮泥沙遥感研究进展与展望

河流悬浮泥沙动态监测对河道变迁、水利工程安全运行、生态和环境保护等都具有重要的应用价值,利用遥感技术可对大区域河流水体悬浮泥沙进行实时监测。与海洋和湖泊等大面积水体相比,当前对河流悬浮泥沙遥感关注较少,且已有研究主要关注入海河流河口区域。为充分发挥不同时间、空间和光谱分辨率多源卫星遥感资料优势,实现更广泛区域、不同级别河流的悬浮泥沙输运遥感监测,对国内外已发表的关于河流悬浮泥沙卫星遥感的数据源和模型进行了系统归纳,总结出河流悬浮泥沙卫星遥感研究中面临河流遥感反射率高精度提取、悬浮泥沙浓度高精度遥感和基于二维表层悬浮泥沙浓度遥感的三维通量遥感等挑战和难点;在此基础上,从去除邻近效应大气校正、考虑悬浮泥沙粒径分布的浓度遥感和悬浮泥沙输运通量三维遥感3个方面对河流悬浮泥沙遥感监测的未来发展进行了展望。     

高分一号遥感数据在冰川变化监测中的应用

针对冰川遥感监测研究工作中存在的数据源选择问题,首次将国产高分一号卫星数据应用于祁连山系青海境内冰川动态变化监测工作.通过国产高分遥感数据与同尺度的Landsat历史数据对比,从变化面积、变化率、空间变化、类型变化、结构变化等方面阐述祁连山系冰川变化规律.研究对比表明：国产高分卫星遥感数据在采集、处理、监测冰川变化中的应用效果完全可以满足1：10万尺度的遥感调查和监测,不管是光谱信息还是纹理信息都优于ETM等中低分辨率数据;国产高分卫星的多光谱数据与Landsat、Aster等历史数据可以形成对比数据集;祁连山系冰川总体变化不大,但在内部结构与时空分布存在较大差异,冰川面积和数量呈双降态势;针对冰川的特殊性,能保证在每年的最佳季节获取最优质的遥感数据,对于时间跨度大的遥感监测目标监测具有重要意义.     

两种被动微波遥感混合像元分解方法比较

被动微波遥感混合像元分解是成功应用被动微波辐射计数据的关键,研究被动微波遥感混合像元分解方法具有重要的应用意义。提出基于地物分类信息的被动微波混合像元分解方法,通过模拟具有不同组分亮温分布的观测地区,将其与具有代表性的Bellerby提出的被动微波混合像元分解方法进行对比研究,分析两种分解方法的适用范围及影响其求解精度的主要因素。Bellerby的方法适合于观测地区中陆地组分亮温不变的情况,当观测地区陆地组分亮温发生变化时,笔者提出的方法能得到更加准确的分解结果。研究结果表明：笔者提出的方法进行混合像元分解后,其陆地组分亮温误差的平均值约为 0.1 K,水体组分亮温误差的平均值约为0.2 K;而采用Bellerby方法计算得到陆地组分亮温误差的平均值约为3.1 K,水体组分亮温误差的平均值约为5.9 K。     

基于MODIS数据的朝鲜半岛土地利用变化信息提取

以朝鲜半岛为研究区域, 基于2000年和2007年的MOD IS数据, 应用线性光谱混合模型进行像元组分分解并提取分类特征, 对像元组分分解后有错分的地物类型结合纹理进行分析, 以决策树模型进行土地利用分类。结果表明, 像元组分分解后的决策树分类结果总体精度达78.346 5% , Kappa系数达0.681 3。与像元组分分解前最大似然法和决策树分类结果相比, 决策树分类精度优于最大似然法, 且像元组分分解方法提高了分类精度。经2000年和2007年像元组分分解后的决策树分类结果对比: 朝鲜耕地面积增加; 韩国耕地面积减少, 居民地面积增加。     

青海木里煤矿区荒漠化的驱动因素及防治对策简

使用多期、多源遥感影像数据,选择青海省木里煤矿区开展了荒漠化遥感监测及变化趋势与采矿相关性分析研究。根据多期高分辨率遥感影像提取了矿业活动区信息。采用多期TM遥感影像,分别利用光谱混合像元分解和生物量遥感估算模型方法,提取了多期土地荒漠化信息。对采矿扰动下荒漠化驱动因素分析结果表明:矿业活动区内开采面、矿山占地增长驱动了荒漠化,矿山关停、复绿治理则扼制并减轻了荒漠化;矿业活动区外荒漠化主要受人类活动、道路修建、保护治理政策等因素影响。根据研究结果,提出了矿区荒漠化防治对策。     

Copper Ore Identification using Spectral Similarity Measurement from Hyperion Image,Mapping of Porphyry Copper Mineralized Zone

This work illustrates the effectiveness of hyperspectal image spectroscopy and lab spectroscopy in identification techniques of minerals in alteration zone of ore body. The adopted procedure involves testing of Hyperion image spectra, their processing for noise, spectral matching and spectral similarity measurement with selected library spectra. Average weighted spectral similarity; visual and statistical matching techniques were used to select end-members from image spectra. Minimum noise fraction and pixel purity index technique were used to retrieve end-member spectra from hyperion image. Hyperspectral image like hyperion has the capability to deliver laboratory standard spectroscopic result. This paper illustrates the capability of hyperion image spectra in copper ore identification and mapping of chalcopyrite outcrop. A systematic approach has been made in this paper. This approach describes how image end member spectra and laboratory spectra can be co-related to fetch accurate spectral form of chalcopyrite ores. Thus, statistical and graphical comparison has been carried out between image derived end member spectra and laboratory spectra of chalcopyrite for better accuracy. The visual measurements is satisfactory, R = 0.973 for fine and 0.976 for medium grained chalcopyrite ore. Excellent statistical significance levels (90–97%) are found while comparing these spectra. There are many success stories of sub-pixel and N-dimensional feature space methods to separate the hydrothermal alteration zones from iron oxide mixed ore bodies. Thus, unmixing methods are very useful for mapping of most dominating mineral parts of a pixel from hyperspectral images which have coarse spatial resolution. Finally, mapping of mineralized zone has been achieved through sub-pixel based classifiers like spectral angle mapper (SAM), constrained energy minimization (CEM) and adaptive coherence estimator (ACE) techniques.     

Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.     

Sub-pixel Mapping of Coarse Satellite Remote Sensing Images with Stochastic Simulations from Training Images

Super-resolution or sub-pixel mapping is the process of providing fine scale land cover maps from coarse-scale satellite sensor information. Such a procedure calls for a prior model depicting the spatial structures of the land cover types. When available, an analog of the underlying scene (a training image) may be used for such a model. The single normal equation simulation algorithm (SNESIM) allows extracting the relevant pattern information from the training image and uses that information to downscale the coarse fraction data into a simulated fine scale land cover scene. Two non-exclusive approaches are considered to use training images for super-resolution mapping. The first one downscales the coarse fractions into fine-scale pre-posterior probabilities which is then merged with a probability lifted from the training image. The second approach pre-classifies the fine scale patterns of the training image into a few partition classes based on their coarse fractions. All patterns within a partition class are recorded by a search tree; there is one tree per partition class. At each fine scale pixel along the simulation path, the coarse fraction data is retrieved first and used to select the appropriate search tree. That search tree contains the patterns relevant to that coarse fraction data. To ensure exact reproduction of the coarse fractions, a servo-system keeps track of the number of simulated classes inside each coarse fraction. Being an under-determined stochastic inverse problem, one can generate several super resolution maps and explore the space of uncertainty for the fine scale land cover. The proposed SNESIM sub-pixel resolution mapping algorithms allow to: (i) exactly reproduce the coarse fraction, (ii) inject the structural model carried by the training image, and (iii) condition to any available fine scale ground observations. Two case studies are provided to illustrate the proposed methodology using Landsat TM data from southeast China.     

基于亚像素角点检测的试样变形图像测量方法

作为一种数字图像测量方法,亚像素角点检测能够在数字图像中可以发现角点的亚像素精度的位置（简称亚像素角点检测方法）。在土工三轴试验中应用该方法使得试样的变形测量更加简便。通过使用高精度标定板,对该数字图像测量方法进行了精度分析,结果表明亚像素角点检测方法的轴向和径向测量的标准差都达到了0.02个像素,相对测量精度达到10-4数量级。在三轴压缩试验条件下,同时使用高精度位移传感器和该测量方法对小变形的干性试样进行了变形测量,证明亚像素角点检测方法对试样变形的测量结果是精确可靠的。     

利用MODIS和AMSR-E进行积雪制图的比较分析

MODIS和被动微波辐射计AMSR-E提供了识别积雪的不同方法.MODIS首先计算反映积雪在1.6μm强吸收特性的归一化差值积雪指数NDSI,在剔除卷云的影响后,得到MODIS积雪分布.AMSR-E则根据积雪在微波波段的差异性散射特性识别积雪.通过案例分析比较了MODIS和AMSR-E积雪分布,发现由于云的遮蔽使MODIS积雪分布面积会比实际小,但由于MODIS的空间分辨率很高,得到的积雪边界线轮廓清晰.而微波由于不受云的影响,得到的AMSR-E积雪分布比较符合实际,但积雪的边界线较粗.     

基于MODIS的青藏高原逐日无云积雪产品算法

青藏高原积雪对高亚洲地区水和能量循环起着重要的反馈和调节作用,其变化影响着融雪性河流流量,对下游水资源和经济活动具有重要影响。中分辨率成像光谱仪（MODIS）具有较高的时空分辨率,被广泛应用于积雪遥感动态监测,然而光学遥感积雪受云层影响严重,且青藏高原地区水汽分布不均,局地对流活跃,积雪的赋存时间变化快,这给高原地区逐日积雪监测及其气候学制图带来挑战。在考虑青藏高原地形和积雪分布特征情况下,结合现有的云覆盖下积雪判别算法,采用8个不同方法的组合,逐步实现MODIS逐日无云积雪算法。选取2009年10月1日-2011年4月30日两个积雪季为研究期,并采用145个地面台站观测雪深数据对去云算法各步骤过程开展精度验证,结果表明：当积雪深度>3 cm时,逐日无云积雪产品总分类精度达到96.6%,积雪分类精度达83%,积雪判对概率（召回率）达到89.0%,算法可实现青藏高原地区逐日无云积雪动态监测和积雪覆盖气候学数据重建,对高亚洲地区的水、生态和灾害等全球环境变化影响研究具有重要的意义。     

Water surface variations monitoring and flood hazard analysis in Dongting Lake area using long-term Terra/MODIS data time series

Dongting Lake is the second largest freshwater lake in China, and its water surface area varied very significantly during last decade. Remote sensing technology has more advantages in macro monitoring of lake water surface area than the traditional methods. In the paper, an integrated threshold method of water body extraction based on MODIS data is given, which synthesizes several factors, including vegetation index—NDVI, spectrum characters of water body, cloud and shadow, and the SRTM digital elevation information. With this method and 356 scenes MODIS 8-Day composite (MOD09Q1) image, water surface area of Dongting Lake was dynamically monitored from 2000 to 2009. The result shows that during 1 year, the water area variation in Dongting Lake area had a typical seasonal (monsoon) behavior, and during last decade, the water area decreased gradually and obviously. Based on variation monitoring, yearly max-submersion time index has been suggested to analyze flood hazard in study area. With the support of ArcGIS software, authors estimated the yearly submersion time of the Dongting Lake for each year separately and average submersion time from 2000 to 2009. The result shows 67.46% of study area is being with high flood hazard.     

基于MODIS影像的鄱阳湖湖面积与水位关系研究

利用统计分析的方法,根据2001年获取的13景鄱阳湖区无云MODIS影像中的9景提取的水体面积,并结合同步观测的水文数据分别采用线性、对数和指数3种模型模拟湖面积-水位之间关系。结果显示对数模型相关性最好（R²=0.918）,其次为线性和指数模型。利用另外4景MODIS影像对模型进行检验表明,该模型精度较高,模拟的最大误差为3.36%。本研究显示,可根据鄱阳湖水位观测值,利用该模型预测鄱阳湖洪涝期洪水淹没面积,以弥补云天状况下光学遥感难以监测到洪水淹没范围的不足。本研究为利用遥感影像实时监控鄱阳湖水情空间动态变化提供了可行的方法,对湖泊、水库的泛洪监测、调洪功能分析具有重要意义。     

湖泊水质遥感研究进展

详述了湖泊遥感水质最新发展动态,如遥感水质模型的数学方法、与水质指标最敏感的波段以及TM、SPOT、MODIS、MERIS、AVHRR、CASI等传感器的适用情况,并分析了可能导致湖泊水质遥感模型误差的原因和解决办法。湖泊各项水质组分与光谱之间相互影响可认为是一种非常复杂的非线性关系,最适合用神经网络这样的黑箱模型来模拟。应当研究和选取敏感波段,用高光谱逐段分析与各种水质指标相关最密切的波段。湖泊水质遥感最终走向实用化必将其与水生态问题结合起来,作为一种监测手段,在水中藻类的时空分布、流域营养物质输送模型和湖泊水域水质模型等问题中得到广泛应用。我国学者使用超光谱数据源获得更为精确的监测成果还比较少,由于我国卫星可以用来进行水质遥感的波段比较宽,应当在新一代的资源环境卫星上加入更适合水质遥感的波段。     

基于光谱-环境随机森林回归模型的MODIS积雪面积比例反演研究

积雪面积比例（Fractional Snow Cover, FSC）数据能在亚像元尺度上定量的描述像元内积雪覆盖的程度,相比二值积雪面积数据可以更加精确地估计积雪覆盖的面积。基于机器学习的随机森林回归模型可以表示高维的非线性关系,可显著提高MODIS FSC的反演精度。采用随机森林回归模型结合光谱、环境信息构建了一个新的回归模型——光谱-环境随机森林回归（Spectral Environment Random Forest Regressor, SE-RFR）模型,用于MODIS数据反演中国区域的FSC。利用中国典型积雪区内由Landsat 8地表反射率数据获取的FSC数据作为参考值,对SE-RFR模型的反演精度进行评估。研究表明,利用“SE-RFR”获取的FSC数据RMSE、MAE分别为0.160、0.104,精度较高。此外,根据SE-RFR模型与未加入环境信息的随机森林回归（S-RFR）模型比较结果可知,加入环境信息的随机森林回归模型提高了FSC反演的精度,特别是在受环境信息影响较大的青藏高原地区,RMSE从0.200降低到0.181。最后,将SE-RFR模型与目前使用广泛的MODIS FSC反演模型FSC_NDSI、MODSCAG和SSEmod进行了比较,结果表明SE-RFR模型的RMSE与FSC_NDSI、MODSCAG和SSEmod模型的RMSE相比,平均RMSE分别提高了12.0%、8.3%和5.5%。总体来说,SE-RFR模型可以准确地提取MODIS FSC,对于区域乃至全球FSC产品制备具有广泛的应用前景。