面向多尺度分割的疑似违章建筑提取算法

针对城市中疑似违章建筑物信息提取的问题,提出了一种基于多尺度分割方法提取疑似违章建筑信息的新算法.该算法首先通过设置多尺度分割参数对遥感影像进行分割,然后通过正射校正、辐射定标、大气校正对遥感影像进行预处理,最后通过多尺度分割实验分析各参数对尺度分割效果的影响.研究结果表明分割尺度为150、形状比例系数为0.7、紧致度...     

基于线性混合光谱方法的时间序列植被覆盖度提取

为了准确地从遥感数据中获取城市植被覆盖度(VFC)信息,以线性混合光谱模型(LSMM)为原理基础,参考各城市端元模型,利用MODIS时间序列提取武汉研究区2007至2014年间植被覆盖度,获得植被覆盖度年际变化情况。在对遥感影像进行端元提取及确定时,结合四种城市端元模型分别对研究区植被覆盖度进行估算,并对像元分解精度作对比分析。结果表明,MODIS时间序列使用混合光谱分析法能够科学客观地实现对研究区植被覆盖度的提取,可以有效地进行周期性监测和分析研究区植被覆盖变化。     

基于辐射传输模型的高分二号影像大气校正方法研究

高分二号卫星的成功发射,标志着我国遥感卫星进入了亚米级高空间分辨率时代,遥感影像在定量反演,地物识别和变化分析等领域将有重要作用.大气校正的精度是影响其定量化应用的重要因素.由于高分二号遥感数据缺乏短波红外波段,无法采用暗像元法进行大气校正.提出一种基于辐射传输模型的高分二号影像大气校正方法,利用6S(second simulation of the satellite signal in the solar spectrum)辐射传输模型建立大气校正系数查找表,利用同步MODIS影像数据结合改进后的暗像元方法反演气溶胶光学厚度,确定大气校正系数,消除高分二号影像大气分子和气溶胶等的吸收和散射的影响,实现GF-2数据的大气校正.选取地表平坦均一的敦煌辐射校正场作为实验区,通过同步的实测数据对校正结果进行精度评价,并且比较大气校正前后归一化植被指数NDVI.结果表明:最小相对误差仅为0.9％,大气校正之后影像数据更真实地反映了地物的反射特性;大气校正后的NDVI大大增强了植被信息反差,突出了GF-2卫星传感器的植被信息区分能力.     

MODIS数据植被覆盖度提取算法比较

植被覆盖度是植物群落覆盖地表状况的一个综合量化指标,是一个重要的生态气候参数。利用MODIS数据对比分析了植被覆盖度常用的提取方法。基于2007年沈阳市的MODIS影像分别使用两种不同的模型（归一化植被指数和增强型植被指数的像元二分模型;三波段梯度差法）提取了植被覆盖度信息,并利用时相相近的高分辨率Landsat TM影像的提取结果对其进行了验证。研究结果表明：对MODIS数据而言,采用像元二分模型能较好地提取大区域范围的植被覆盖度信息。     

自适应形态学与多尺度结合的植被区域遥感图像分割方法

为减小植被区域遥感图像分割误差,解决植被区域中待分割目标因覆盖度和噪声等因素造成的过分割和欠分割问题,提出了一种自适应形态学与多尺度结合的植被区域遥感图像分割方法。首先,通过general adaptive neighborhood(GAN)结构元素构造膨胀和腐蚀运算,推导出GAN形态学开、闭运算;然后,构造一种GAN形态学复合型滤波器,填充植被覆盖度不足的孔洞,减小噪声对图像的干扰;最后,通过多尺度分割算法,对遥感图像植被区域进行分割。实验结果表明：所提方法能够有效避免欠分割和过分割现象且能对遥感图像植被区域进行准确分割;与传统多尺度分割和传统形态学与多尺度结合方法相比,所提方法的分割误差较小。     

基于支持向量值轮廓波变换的遥感影像融合

 本文研究基于支持向量值轮廓波变换的遥感影像融合.首先将支持向量值滤波器和方向滤波器组进行合理搭配,构造支持向量值轮廓波变换,该变换具有平移不变、泛化能力好、捕捉奇异性能强等特性.然后利用该变换对多源遥感影像进行多尺度、多方向、多分辨率分解,在不同的分解水平上利用基于区域能量和基于区域的轮廓波对比度方法进行融合.实验结果表明,利用该变换进行遥感影像融合时,能在不降低空间分辨率的情况下有效保留源影像的光谱信息.     

多源极轨气象卫星热红外波段数据的同化方法研究

为保证多源极轨气象卫星热红外波段数据的可比性与进一步的定量应用,从辐射和几何两个方面进行了数据同化方法研究.通过辐射定标、卫星天顶角校正和通道光谱响应差异的校正,实现了不同卫星探测辐射值的直接对比;在扫描行空间分辨率归一化的基础上,进行投影变换几何粗校正和图像平移精校正,使多源数据在几何位置上能得以高精度的配准.     

耦合辐射传输模型的植被含水量遥感改进监测

基于耦合的叶片与冠层辐射传输模型,研究并使用了改进的植被指数提取植被含水量,以实现植被水分含量的遥感监测.使用PROSAIL模拟的高光谱数据,首先对NDVI、WI、NDWI、MSI、CSI及NDII等已有的植被指数提取含水量进行可行性分析比较.结果显示NDVI不能估算出植被含水量,CSI估算能力也不理想,其余四个指数WI,NDWI,NDII,MSI估算植被含水量时相关系数平方在0.76左右;之后对NDWI提取植被含水量进行了敏感因子分析,结果表明NDWI与MCARI具有较好的线性关系,从而得出改进的植被含水量估算方法.该方法可以较好地去除LAI的干扰,估算的相关系数平方提高为0.97,增加了27%,估算残差由初始的0.0156降低为0.00535,减少了65%.     

基于分形理论与GEOEYE-1影像的NDVI连续空间尺度转换模型构建及应用

尺度效应是定量遥感领域一个重要而基础的科学问题。尺度效应的解决方法为尺度转换。传统的尺度转换方法面临涉及的空间分辨率离散、有限及传感器间成像参数归一化的干扰等问题。在提出并实践基于分形理论构建归一化差分植被指数（Normalized Difference Vegetation Index, NDVI）连续空间尺度转换模型（continuous spatial scaling model, CSSM）的基础上,本文基于GEOEYE-1多光谱影像、以NDVI为研究对象继续就分形方法在高空间分辨率基础图影像中的适用性进行研究,并就分形模型构建最合理尺度层级的确定进行细致探讨。实验获得如下结论：（1）基于分形理论的NDVI连续空间尺度转换模型构建方法适用于高空间分辨率遥感影像;（2）在给定的条件下,NDVI尺度转换分形模型构建时最合理尺度层级存在且可计算。论文的研究使得分形方法所适用的反演量类型及基础图空间分辨率范围皆有大的扩展。     

基于时间序列遥感数据的植被覆盖度反演研究

基于案例分析,阐述时间序列遥感数据的植被覆盖度反演,结合GIS和RS技术,采用像元二分模型提取估算植被覆盖度,通过数理统计方法得到植被覆盖率的变化规律,从而为研究城市生态环境变化状况提供参考,并对城郊地表环境的改善及建设提供参考依据。     

WorldView-2影像的湿地典型挺水植物群落含水量估算研究——以北京野鸭湖湿地为例

利用多光谱遥感技术定量估算野鸭湖湿地挺水植物的含水量.基于典型挺水植物的实测冠层光谱及其对应样方的叶片含水量和叶面积指数LAI数据,首先对芦苇和香蒲的地面实测光谱进行重采样,以模拟WorldView-2影像的光谱,然后利用模拟光谱分别构建芦苇和香蒲任意两波段反射率组合而成的比值(SR)和归一化差值植被指数(NDVI),通过分析植被指数与CWC(冠层含水量,Canopy Water Content)的相关关系,选择与CWC显著相关的植被指数,并通过单变量线性与非线性拟合的分析方法确定监测不同挺水植物群落的最佳植被指数,建立估算模型;结合覆盖研究区的WorldView-2高分辨率多光谱影像,对研究区的挺水植物群落CWC进行反演及制图.结果表明,基于模拟WorldView-2影像光谱构建的比值(SR)和归一化差值植被指数(NDVI)与CWC的总体相关性较高;SR(8,3)芦苇为估算CWC芦苇的最优植被指数,估算模型为y=0.005x+0.003,NDVI(8,3)香蒲为估算CWC香蒲的最优植被指数,估算模型为y=2.461x2-0.313x+0.032,通过交叉检验,CWC芦苇和CWC香蒲的预测精度分别为87.42%和82.12%,预测精度较为理想;利用实测数据对反演的CWC空间分布图进行了验证,通过验证,芦苇和香蒲影像估算CWC的均方根差(RMSE)分别为0.0048和0.0052,估算精度分别为83.56%和80.31%,表明利用WorldView-2高分辨率多光谱影像反演湿地挺水植物群落CWC具有较高的可行性.     

Monitoring detailed land surface changes using an airbornemultispectral digital camera system

Airborne multispectral digital camera systems provide the potential for flexible and inexpensive monitoring of land surface changes at high spatial resolutions. The goal of this study was to characterize the radiometric properties and processing requirements of Airborne Data Acquisition and Registration (ADAR) System 5500 data in the context of environmental monitoring applications. This airborne multispectral digital imaging system, manufactured and operated by Positive Systems Inc., consists of four digital cameras and an on-board digital capture system. Sensor lab tests of the digital cameras were conducted to determine spatial uniformity of image brightness, high frequency noise and signal-noise ratio (S/N), linearity of radiometric response, spectral response, and radiometric normalization and calibration. Submeter resolution ADAR 5500 data were acquired over constructed and natural coastal marshes, concurrent with ground-level radiometric and plant measurements, ADAR 5500 data were found to have high radiometric fidelity and were successfully normalized and calibrated using simple procedures and knowledge that the radiometric response is linear. Noise levels were low, and S/N was about 120 for lab test images and 90 for airborne images of the marsh complex. Vignetting effects were evident in lab images, which were inverted and low-pass filtered to generate a correction mask. Spatial and temporal differences of spectral reflectance between important marsh vegetation cover types represent “environmental signals” in the context of monitoring habitat restoration projects     

基于艇载遥感成像数据的高空间分辨率全色遥感图像仿真

高空间分辨率全色遥感图像在军事侦察、地面监视等领域具有较高的应用价值.为模拟星载全色遥感图像, 提出了一种由艇载遥感成像系统获取的低空遥感图像为数据源的高空间分辨率全色遥感图像仿真方法.首先将低空宽视场图像按典型地物类型进行监督分类, 其次将低空宽视场图像与多光谱图像按不同地物类型分类拟合, 并将多光谱拟合结果合成高空间分辨率全色仿真图像, 最后对高空间分辨率全色仿真图像进行仿真精度评价.相比星载全色遥感图像, 仿真图像同样具备高空间分辨率、全色波段、宽视场等特点.仿真方法可为星载全色遥感图像仿真提供较准确的数据支撑.     

城市生态环境基础状况遥感信息提取研究——以上海市中心城区为例

综合考虑遥感解译能力和生态环境效应,将城市生态环境基础状况分为八大类型.以上海市中心城区为例,探讨城市生态环境基础状况的遥感影像信息提取方法.以TM与SPOT融合影像为数据源,基于已有的GIS数据库中对八大类的目视解译分类知识,建立了不同类型在融合影像的三色合成图像和融合图像不同波段比值的彩色合成图像的分类回归树识别方法,实现了大范围城市居住区信息,水体信息,以及绿化和农业四类信息提取.     

基于视觉显著模型的遥感图像舰船快速检测

为了降低传统高分辨率海面遥感图像舰船目标检测方法的计算复杂度,提高检测速度,在舰船目标检测中引入了基于直方图对比度的视觉显著模型和空间降维算法,提出一种新的高分辨率海面遥感图像舰船目标快速检测算法。首先对高分辨率遥感图像进行空间降维,然后计算降维图的视觉显著图,突出感兴趣目标区域,最后利用最大类间方差法分割视觉显著图以获取舰船目标候选区域。结果表明,目标检测所消耗的时间减小为原来的10%~12%,弱化了复杂海面纹理背景对目标检测的影响。该研究提高了高分辨率遥感图像舰船目标的检测效率。     

Removal of terrain effects from SAR satellite imagery of Arctictundra

Synthetic aperture radar (SAR) images of the Earth's terrestrial surface contain geometric and radiometric image effects which are caused by varying terrain elevation and slope. The radiometric effects tend to mask signal variations caused by other physical variables such as soil moisture and surface vegetation type, which are known to influence SAR backscatter signals. As a result, raw SAR images are of limited use in classifying surface vegetation type or quantifying the spatial distribution of soil moisture in regions of terrain relief, The authors present a technique for removing radiometric terrain effects from SAR images. Image correction was carried out in two steps. First, an existing modeling package was used in combination with digital elevation data in order to map the raw image pixels onto a geodetic coordinate system, thereby removing the geometric portion of the image distortion. Radiometric effects were then removed with the aid of a backscatter model which treats the reflected radiation as a combination of diffuse-Lambertian and specular components. Parameters in the backscatter model were determined by comparing two C-band SAR images of a test area in a region of Arctic tundra which were taken from ascending and descending orbit tracks of the ERS-1 satellite. The ascending and descending images displayed reductions in pixel value variance of 30% and 13%, respectively, after processing. Direct comparison of the two test area images reveals a dramatic improvement in image similarity after processing     

Estimation and monitoring of bare soil/vegetation ratio with SPOT VEGETATION and HRVIR

Covering soils with vegetation during the fallow and planting seasons is one of the main ways to reduce water pollution, by restricting pollutant fluxes to aquatic systems. The bare soil/vegetation ratio monitoring can be carried out daily with a coarse spatial resolution using SPOT VEGETATION (1 km). Nevertheless, land-cover changes detected at a regional scale with this ratio may be due to winter vegetation cover changes as well as the influence of climatic events. Therefore, observed changes have to be validated from a local-scale analysis with higher spatial resolution data. The aim of this study is to develop a technique that allows high or low variations detected at a regional scale to be assessed from SPOT VEGETATION images with data acquired at a higher scale, SPOT High Resolution Visible and Infrared images in our case. In this study, the link between the images from the two sensors is achieved from the design of an artificial neural network method based on a Kohonen self-organizing map. The originality of this method lies in the use of external knowledge from ground observations and the use of temporal behavior to solve such a change of scale. Results of testing this method by using a potential change map based on the last few years' land-cover observations have shown a good correspondence between the observed and predicted bare soil/vegetation balance with regards to the spatial resolution difference between the two sensors.