基于资源三号卫星融合影像的地形图修测技术

设计了基于资源三号卫星融合影像修测1∶5万地形图的方案。根据方案首先对试验区资源三号卫星影像进行正射纠正、影像融合等影像处理,然后将软件Mapstore用于资源三号卫星融合影像进行地形图修测,经过遥感影像目视解译、人机交互操作、后期编辑等处理,完成了1∶5万矢量地形图修测。试验结果表明,修测设计方案合理,资源三号卫星融合影像修测地图精度能达到国家成图规范要求,可用于1∶5万地形图修测。     

资源三号卫星影像融合优化方法探讨

针对资源三号卫星影像如何进行影像融合、波段组合才能到达到最佳视觉效果还没有统一意见的问题,该文提出了利用目前测绘项目中普遍采用的融合算法、波段组合比例对不同地形类别资源三号卫星影像进行实验验证的方法。综合主观评价与客观评价融合后的影像质量,得出了Subtractive、Pansharp和Pansharp2方法较为适合资源三号卫星影像融合的结论,其中绿波段与近红外波段的最佳组合比为9∶1。同时,分析了资源三号卫星影像融合后数据量大大增加、如何将近红外波段的波谱信息合理地加入到红、绿、蓝波段中等问题。     

资源三号测绘卫星提供影像在线服务

正资源三号测绘卫星自2012年1月9日成功发射以来,截至5月18日,资源三号测绘卫星已在轨运行130天,共计绕地飞行1985圈,编制成像计划268轨,实际接收517轨原始影像,原始数据量已超70TB;中国境内有效影像覆盖面积     

缅甸毛淡棉地区高分一号卫星影像

正高分一号卫星是中国高分辨率对地观测系统的首发星,配置了2台2m分辨率全色和8m分辨率多光谱相机以及4台16m分辨率多光谱宽幅相机,具有高、中空间分辨率对地观测和大幅宽成像结合的特点,其2m分辨率全色和8m分辨率多光谱图像组合幅宽优于60km,16m分辨率多光谱图像组合幅宽优于800km,为国际同类卫星观测幅宽的最高水平,大幅提升了观测能力,对大尺度地表观测和环境监测具有独特优势。封面图片是高分     

利用像素工厂处理资源三号卫星影像

我国自主研发的资源三号测绘卫星已经在轨运行半年多,该卫星图像具有分辨率高、图像几何精度和目标定位精度较高等特点,尤其是1∶5万比例尺的立体测图能力在国际上有很强的竞争力。本文主要阐述利用像素工厂进行资源三号卫星影像的快速处理技术和流程。     

天绘一号卫星影像融合及其质量评价

以南京市天绘一号卫星影像为实验数据,运用IHS变换、Brovey变换、Wavelet变换、PCA、Pansharp等遥感影像融合方法对其全色和多光谱影像进行融合,并对5种方法的融合结果进行了定性和定量分析。实验结果表明,IHS变换法和Pansharp法能显著增强融合影像的光谱信息,提高融合影像的空间分辨率。     

基于SVM的资源三号测绘卫星影像分类

以江苏省宜兴市为研究区,利用支持向量机(SVM)方法对资源三号测绘卫星影像进行了分类,其总分类精度为97.76%,Kappa精度为0.968 7。为了评价算法的适用性,同时应用最大似然法与最小距离法对同一影像进行分类测试,支持向量机分类法精度高于其他2种方法,可以满足土地覆盖分类调查需求。     

资源三号卫星全色与多光谱影像融合方法

在处理新型高分辨率遥感卫星影像时,传统的像素级融合方法会引起绿色植被区域较大的光谱扭曲.本文提出一种适合资源三号卫星影像的融合新方法,可以很好地解决此问题.首先对全色图像进行低通滤波并下采样得到退化后的图像HP,利用回归方法建立多光谱影像与图像HP的方程求解回归系数,考虑到绿色植被颜色失真严重,采用NDVI进行分类,分别求取绿地和非绿地的回归系数进行分类融合.试验结果证明,该方法在提高多光谱影像分辨率的同时,尽可能多地保持了光谱信息,特别是绿色植被区域,融合效果要好于其他方法.     

资源三号卫星多光谱影像的姿态角常差检校

针对资源三号卫星姿态角常差导致的多光谱影像定位精度降低的问题,该文在构建多光谱影像严格几何定位模型的基础上,提出了运用罗德里格矩阵建立姿态角常差检校方法;根据少量地面控制点求出姿态角常差构成的偏置矩阵后,使影像定位精度得到显著提高。实验结果证明,该方法具有较好的应用价值。     

资源三号卫星影像最优区域覆盖算法研究

利用资源三号卫星(01星、02星)影像进行目标地理区域的立体测图及数字高程模型生产时,在满足同轨立体及数据质量要求的前提下,要求影像尽可能完全覆盖目标空间区域,且影像数量尽量少。如何从大量冗余的影像中获取一组覆盖目标地理区域的最优的影像组是数据生产前期需要解决的一个关键问题。目前,没有一种成熟的算法能够获取最优影像组,生产人员还是采用人工挑选数据的方式获取目标影像。为解决该问题,提出了一种空间切割方式的最优区域覆盖算法,利用影像有效空间范围将目标研究区域连续地切割,直到目标区域被完全切割完,获取最优影像集合。利用资源三号卫星01星影像数据进行算法验证,实验结果论证了本算法较好地解决了影像覆盖区域数据挑选的问题。     

全球30m分辨率多光谱影像数据自动化处理的实践与思考

全球地表覆盖遥感制图与关键技术研究项目要求对两个基准年度(2000年、2010年)全球30 m分辨率的多光谱遥感数据进行辐射处理和几何精纠正处理,为地表覆盖制图完成数据准备。数据以Landsat TM/ETM+为主,HJ-1A/B CCD数据为补充,共计2万多景影像需要进行辐射处理,有1000多景HJ-1A/B CCD影像需要几何精纠正。如此大规模的数据处理,自动化处理是必然的选择。本文介绍了HJ-1A/B CCD图像几何精纠正自动化实现中关键问题的解决方法和精度评价结果,Landsat TM/ETM+和HJ-1A/B CCD图像自动化辐射校正中关键问题的解决方法和精度评价结果,以及大规模的数据处理活动引发的一些思考。     

面向对象分类提取高分辨率多光谱影像建筑物

初步测试利用基于知识规则的面向对象分类方法从高分辨率Ikonos卫星影像上提取建筑物,包括:融合1 m全色和4 m多光谱波段影像,生成1 m分辨率的多光谱融合影像;分割融合影像;利用影像对象的光谱和空间特征执行基于对象的分类。面向对象分类提取结果与传统的基于像元最大似然分类结果进行对比,表明面向对象分类方法更适用于提取高分辨率遥感影像中的建筑物。     

Object-based habitat mapping using very high spatial resolution multispectral and hyperspectral imagery with LiDAR data

This study investigated the combined use of multispectral/hyperspectral imagery and LiDAR data for habitat mapping across parts of south Cumbria, North West England. The methodology adopted in this study integrated spectral information contained in pansharp QuickBird multispectral/AISA Eagle hyperspectral imagery and LiDAR-derived measures with object-based machine learning classifiers and ensemble analysis techniques. Using the LiDAR point cloud data, elevation models (such as the Digital Surface Model and Digital Terrain Model raster) and intensity features were extracted directly. The LiDAR-derived measures exploited in this study included Canopy Height Model, intensity and topographic information (i.e. mean, maximum and standard deviation). These three LiDAR measures were combined with spectral information contained in the pansharp QuickBird and Eagle MNF transformed imagery for image classification experiments. A fusion of pansharp QuickBird multispectral and Eagle MNF hyperspectral imagery with all LiDAR-derived measures generated the best classification accuracies, 89.8 and 92.6% respectively. These results were generated with the Support Vector Machine and Random Forest machine learning algorithms respectively. The ensemble analysis of all three learning machine classifiers for the pansharp QuickBird and Eagle MNF fused data outputs did not significantly increase the overall classification accuracy. Results of the study demonstrate the potential of combining either very high spatial resolution multispectral or hyperspectral imagery with LiDAR data for habitat mapping.     

高分辨率遥感影像的全变分分割模型

由于全变分(Total Variation,TV)模型具有较好的去噪、增强和扩散等功能,在过去的几十年中,TV模型在图像去噪、增强和超分辨率重建等方面得到了深入研究与广泛应用。鉴于TV模型的理论与分割理论具有一致性,因此本文主要研究TV模型用于高分辨率遥感影像的分割,并针对地物多尺度特征,提出了自适应的TV(ATV)模型;且与目前流行的面向对象的影像分析软件eCognition中的FNEA分割方法进行了比较。实验采用2幅高分辨率遥感影像,同时采用了面向对象的分割和分类评价,得出各方法各具优缺点的结论。     

Automated detection and mapping of crown discolouration caused by jack pine budworm with 2.5 m resolution multispectral imagery

Jack pine budworm (Choristoneura pinus pinus (Free.)) is a native insect defoliator of mainly jack pine (Pinus banksiana Lamb.) in North America east of the Rocky Mountains. Periodic outbreaks of this insect, which generally last two to three years, can cause growth loss and mortality and have an important impact ecologically and economically in terms of timber production and harvest. The jack pine budworm prefers to feed on current year needles. Their characteristic feeding habits cause discolouration or reddening of the canopy. This red colouration is used to map the distribution and intensity of defoliation that has taken place that year (current defoliation). An accurate and consistent map of the distribution and intensity of budworm defoliation (as represented by the red discolouration) at the stand and within stand level is desirable.Automated classification of multispectral imagery, such as is available from airborne and new high resolution satellite systems, was explored as a viable tool for objectively classifying current discolouration. Airborne multispectral imagery was acquired at a 2.5 m resolution with the Multispectral Electro-optical Imaging Sensor (MEIS). It recorded imagery in six nadir looking spectral bands specifically designed to detect discolouration caused by budworm and a near-infrared band viewing forward at 35° was also used. A 2200 nm middle infrared image was acquired with a Daedalus scanner. Training and test areas of different levels of discolouration were created based on field observations and a maximum likelihood supervized classification was used to estimate four classes of discolouration (nil-trace, light, moderate and severe). Good discrimination was achieved with an overall accuracy of 84% for the four discolouration levels. The moderate discolouration class was the poorest at 73%, because of confusion with both the severe and light classes. Accuracy on a stand basis was also good, and regional and within stand discolouration patterns were portrayed well. Only three or four well-placed spectral bands were needed for a good classification. A narrow red band, a near-infrared and short wave infrared band were most useful. A forward looking band did not improve discolouration estimation, but further testing is needed to confirm this result.This method of detecting and classifying discolouration appears to provide a mapping capability useful for conducting jack pine budworm discolouration surveys and integrating this information into decision support systems, forest inventory, growth and yield predictions and the forest management decision-making process.     

高分辨率遥感影像融合研究

遥感影像融合不仅可以提高原多光谱影像的空间分辨率,更重要的是最大量地保留影像的光谱信息。为了研究适合于QuickBird遥感影像融合的融合方法,本研究应用乘法复合算法(MLT)、改进的Brovey(MB)、高通滤波(HPF)以及基于平滑滤波的亮度调节算法(SFIM)四种融合方法对QuickBird影像进行了融合试验和分析。试验区以覆盖不同土地利用类型的一小景QuickBird影像为基础。采用了均值偏差、标准差、信息熵、平均梯度和相关系数五种数字统计方法来定量地评价由以上算法产生的融合影像。分析结果表明:SFIM算法在光谱保真性、高频信息融入度、影像清晰度方面都优于其他三种方法。因此,在研究的四种方法中,SFIM算法最适合Quick-Bird影像融合。     

面向对象的高分辨率影像特征选择研究

本文主要研究面向对象的高分辨影像信息提取中的特征选择问题.文中分别选择光谱、纹理、形状等特征57个和28个进行特征优化,得到两组分别由46个和4个特征组成的不同的最优特征集,并利用这些特征集采用K近邻、模糊与K近邻级联两种不同的面向对象分类策略进行分类研究.最后从合理性、效率和精度三方面进行了对比分析.实验结果表明,对...     

Landsat 8多光谱数据辅助下的高分影像多尺度分割

为解决高分影像分割的边缘锯齿性明显等问题,本文以黑龙江省伊春市桦皮羌子林场为研究区开展了有无多光谱数据辅助的高分影像分割对比试验。首先,本文设计了多尺度分割算法的相同尺度参数下分割试验,确定了该算法分割GF-2影像时应采用的最佳同质性准则组合参数;然后,基于影像分割对象同质性局部方差变化率反映最优分割尺度的思想,利用ESP2工具找出固定尺度范围内的最优分割尺度范围;最后执行最佳同质性准则组合参数配合下的最优分割尺度范围内各个尺度下的多尺度分割,并采用矢量距离指数、紧密度指数、形状指数对2种分割试验结果进行了评价。结果表明,与GF-2影像独立分割相比,Landsat 8多光谱数据辅助下的GF-2影像分割在矢量距离指数、紧密度指数、形状指数的质量上均有提升,平均提升率分别为8.05%、28.40%、11.76%。     

Object based building footprint detection from high resolution multispectral satellite image using K-means clustering algorithm and shape parameters

Object-based image analysis (OBIA) has been a new area of research in satellite image processing applications, since it improves the quality of information acquisition about geospatial objects and also enables to add spatial and contextual information to the objects of interest. The extraction of buildings from High Resolution Satellite (HRS) image in an urban scenario has been an intricate problem due to their different size, shape, varying rooftop textures and low contrast between building and surrounding region. In this study, a new object-based automatic building extraction technique has been proposed to extract building footprints from HRS pan sharpened IKONOS multispectral image. The study is mainly emphasizing on obtaining optimal values for segmentation parameters, shape parameters, and defining rule set to extract buildings and eliminate misclassified other urban features. The suitability of the technique has been judged using different indicators, such as, completeness, correctness and quality.