LBV变换在国产ZY-3卫星影像中应用研究探讨

根据国产资源三号(ZY-3)卫星遥感影像光谱特征,提出并推导适用于ZY-3遥感影像的LBV变换公式,探讨该方法在提高国产ZY-3卫星影像质量的可行性。首先,针对ZY-3遥感影像特点,选择9类典型地物光谱信息,通过回归分析求解回归系数;然后,根据影像典型地物空间(裸地、水体、植被)、色彩空间(红、绿、蓝)及LBV变量空间(地物总体辐射水平、可见光—近红外辐射平衡、辐射变换矢量)之间特点计算推导ZY-3卫星影像的L,B,V这3个分量;最后,利用福建省宁德市ZY-3遥感影像进行实验,定量分析评价实验结果。结果表明:①从目视效果看,相比原始影像,变换后影像更加清晰,层次感更强,细节信息也更为丰富,从而更有利于后续地物的判定、识别;②该方法得到的影像信息熵为6. 21,平均梯度为4. 71,偏差系数为0. 46,变换后遥感影像质量较好;③该方法对ZY-3遥感影像分类的总体精度最高达89. 71%,Kappa系数最高为0. 875 3,分类精度较高。因此,该方法能很好地提高ZY-3遥感影像质量,可用于ZY-3遥感影像处理及后续信息提取工作。     

矿山遥感监测中ZY-102C影像融合方法对比研究

基于ZY-1 02C数据开展矿产资源开发多目标遥感调查与监测,运用PCA,HSV,Brovery和GramSchmidt等融合方法对ZY-1 02C影像进行融合,对每种融合方法的融合效果进行定性与定量分析评价。研究结果表明,Gram-Schmidt融合方法是一种适合于矿产资源开发多目标遥感调查与监测的ZY-1 02C数据融合方法。     

修正的Gram-Schmidt正交化广义逆平差方法

直接从条件方程或误差方程系数阵入手,利用修正的Gram-Schmidt正交化过程对系数阵进行三角分解,实现最小二乘求解,导出了基于修正的Gram-Schmidt正交化过程求解系数阵广义逆的数学公式和计算步骤,给出了通过广义逆表示的未知数解向量及其协因数阵的数学表达式。计算过程不仅避免了对矩阵的求逆,并从理论上解决了Gram-Schmidt正交化方法由于舍入误差的影响表现出的数值不稳定性问题,从而很好地解决了具有秩亏系数阵方程组解的不唯一性。算例结果表明,基于修正的Gram-Schmidt正交化方法可以处理包括秩亏阵在内的任意矩阵;在处理不设起算数据的变形监测网观测数据时,能够方便地获得其经典解、伪逆解或拟稳解,而不需要重复计算。     

ZY-1 02C星图像与ETM~+图像融合方法及效果评价

资源一号(ZY-1)02C卫星的发射成功填补了我国自主研发高分辨率遥感卫星的空白。根据该卫星的图像特点和数据质量,以俄罗斯西伯利亚地区的ZY-1 02C卫星P/MS传感器数据为例,通过解译该地区的地质矿产信息,对P/MS图像数据的处理和质量情况进行分析;同时结合该地区的美国Landsat卫星ETM+数据,将P/MS全色波段数据与ETM+多光谱数据融合,并采用HSV,Brovery,Gram-Schmidt和PCA等4种融合方法进行实验对比。实验结果表明,用Gram-Schmidt法融合后的图像,光谱信息丢失最少,融合效果最好,可有效提高融合图像的空间分辨率,进而提高该地区的遥感地质解译精度。该研究成果将为进一步推进我国国产高分辨率遥感卫星的应用发挥积极作用。     

基于高分一号对资源三号卫星交叉定标的研究

为了研究在不依靠国外辐射基准卫星为参考的情况下,基于国产高分辨率卫星对资源三号(ZY-3)卫星交叉定标的可行性,该文提出了一种基于国产高分辨率卫星高分一号(GF-1)的交叉定标方法。实验对ZY-3多光谱传感器MUX进行了两次交叉定标实验:第一次以GF-1PMS为参考传感器;第二次以GF-1 WFV2传感器为参考。两次交叉定标结果与MUX场地定标系数对比,以PMS为参考的交叉定标相对误差在4个波段分别为-1.16%、3.19%、1.62%和5.64%;以WFV2为参考的交叉定标相对误差在4个波段分别为2.83%、17.30%、20.85%和7.54%。采用Landsat-8数据作为验证参考,利用PMS-MUX交叉定标结果反演辐亮度值,在4个波段的最大相对误差为-6.88%。经过分析讨论,PMS-MUX交叉定标结果的不确定度为6%。     

基于缨帽变换的农田洪水淹没范围遥感信息提取

针对灾后水中悬浮物质增多和高含水量农作物导致常规水体信息提取方法精度较低的问题，提出了一种基于缨帽变换的农田洪水淹没范围遥感信息提取方法。首先，对灾前、灾后遥感图像进行辐射定标和大气校正。其次，通过缨帽变换获取绿度分量和湿度分量；然后，利用最大类间方差法对湿度分量进行分割，结合绿度分量提取水体信息；最后，叠加农田矢量数据，确定农田洪水淹没范围。以湖南省岳阳市及其附近区域为研究区，从定性和定量两个方面对方法进行精度评价。结果表明，该方法所得结果边界清晰，范围准确，生产者精度和用户精度分别为0.97和0.90。该研究能够为农田灾损评估、洪涝灾害动态监测提供参考。     

Landsat 8陆地成像仪影像的缨帽变换推导

针对Landsat 8陆地成像仪影像,该文提出了一种缨帽变换系数的推算方法。利用准同步的Landsat ETM+影像缨帽变换结果为目标值,采用普鲁克算法对不同地理分布下的陆地成像仪影像样本点的主成分轴分别进行旋转,得到相应的初始变换系数;通过广义普鲁克分析对所得初始变换系数进行平均化,并以格拉姆-施密特算法保持其正交性,最终得到基于陆地成像仪大气表观反射率数据的缨帽变换系数。验证结果表明,与现有陆地成像仪变换系数相比,该文提出的缨帽变换精度相对较高,误差较小,变换后的不同地物覆盖类型具有更好的可分性。     

资源一号02C星遥感影像融合分析

资源一号02C星搭载了我国民用遥感卫星最高分辨率的多光谱相机,同时还搭载有分辨率高达2.36m的高分辨率相机。基于Gram-Schmidt变换的遥感图像融合方法得到的融合图像,空间分辨率大大提高,同时与原多光谱波段的光谱信息相关性高,光谱保真能力强。相较于主成分分析方法、HSV变换、Brovey方法其更适用于资源一号02C星遥感数据的融合处理。     

特征变换的延安建筑物提取研究

针对高空间分辨率卫星遥感数据多光谱波段数较少和原始波段组合光谱特征利用有限等问题,该文提出了一种基于特征变换的建筑物信息提取方法。以陕西省延安市宝塔地区为研究区,基于快鸟数据采取特征变换、波段选择、数据融合等解决高空间分辨率原始光谱特征利用有限等问题,采用知识规则的面向对象分类方法进行建筑物识别研究。实验表明,缨帽变换波段能有效地突出建筑物信息,4种融合算法中主成分变换融合适用进一步面向对象分类,建筑物识别的总体精度达到89.3%。此方法能有效识别沿坡脚或滑坡体分散分布的建筑物,为快速获取居民空间分布信息和辅助灾害应急评估等提供参考。     

不同空间分辨率遥感影像反立体现象纠正的研究

以陕北县南沟流域为研究区域,运用基于DEM和IHS变换的遥感影像反立体纠正的方法,对空间分辨率不同的TM遥感影像与资源三号卫星遥感影像(ZY-3)进行反立体纠正,比较纠正后的正立体影像。实验结果表明,采用同种方法对同一地区的不同空间分辨率的遥感影像进行反立体纠正,其正立体效果是迥然不同的,ZY-3遥感影像的纠正效果要优于TM遥感影像。     

Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor

Bracken fern is an invasive plant that presents serious environmental, ecological and economic problems around the world. An understanding of the spatial distribution of bracken fern weeds is therefore essential for providing appropriate management strategies at both local and regional scales. The aim of this study was to assess the utility of the freely available medium resolution Landsat 8 OLI sensor in the detection and mapping of bracken fern at the Cathedral Peak, South Africa. To achieve this objective, the results obtained from Landsat 8 OLI were compared with those derived using the costly, high spatial resolution WorldView-2 imagery. Since previous studies have already successfully mapped bracken fern using high spatial resolution WorldView-2 image, the comparison was done to investigate the magnitude of difference in accuracy between the two sensors in relation to their acquisition costs. To evaluate the performance of Landsat 8 OLI in discriminating bracken fern compared to that of Worldview-2, we tested the utility of (i) spectral bands; (ii) derived vegetation indices as well as (iii) the combination of spectral bands and vegetation indices based on discriminant analysis classification algorithm. After resampling the training and testing data and reclassifying several times (n = 100) based on the combined data sets, the overall accuracies for both Landsat 8 and WorldView-2 were tested for significant differences based on Mann-Whitney U test. The results showed that the integration of the spectral bands and derived vegetation indices yielded the best overall classification accuracy (80.08% and 87.80% for Landsat 8 OLI and WorldView-2 respectively). Additionally, the use of derived vegetation indices as a standalone data set produced the weakest overall accuracy results of 62.14% and 82.11% for both the Landsat 8 OLI and WorldView-2 images. There were significant differences {U (100) = 569.5, z = −10.8242, p < 0.01} between the classification accuracies derived based on Landsat OLI 8 and those derived using WorldView-2 sensor. Although there were significant differences between Landsat and WorldView-2 accuracies, the magnitude of variation (9%) between the two sensors was within an acceptable range. Therefore, the findings of this study demonstrated that the recently launched Landsat 8 OLI multispectral sensor provides valuable information that could aid in the long term continuous monitoring and formulation of effective bracken fern management with acceptable accuracies that are comparable to those obtained from the high resolution WorldView-2 commercial sensor.     

Comprehensive study of the biophysical parameters of agricultural crops based on assessing Landsat 8 OLI and Landsat 7 ETM+ vegetation indices

For three agricultural crop types, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), we estimated biophysical parameters including fresh and dry biomass, leaf area index (LAI), and vegetation water content, for which we found the equivalent water thickness (EWT), fuel moisture content per fresh weight (FMC_FW), and fuel moisture content per dry weight (FMC_DW). We performed these estimations using data from the newly launched Landsat 8 Operational Land Imager (OLI) sensor, as well as its predecessor the Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Progress in the design of the new sensor (i.e., Landsat 8), including narrower near-infrared (NIR) wavebands, higher signal-to-noise ratio (SNR), and greater radiometric resolution highlights the necessity to investigate the biophysical parameters of agricultural crops, especially compared to data from its predecessor. This study aims to evaluate vegetation indices (VIs) derived from the Landsat 8 OLI and the Landsat 7 ETM+. Both the Landsat 8 OLI and Landsat 7 ETM+ VIs agreed well with in-situ data measurements. However, the Landsat 8 OLI-derived VIs were generally more consistent with in situ data than the Landsat 7 ETM+ VIs. We also note that the Landsat 8 OLI is better able to capture the small variability of the VIs because of its higher SNR and wider radiometric range; in addition, the saturation phenomenon occurred earlier for the Landsat 7 ETM+ than for the Landsat 8 OLI. This indicates that the new sensor is better able to estimate the biophysical parameters of crops.     

Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping

An image dataset from the Landsat OLI spaceborne sensor is compared with the Landsat TM in order to evaluate the excellence of the new imagery in urban landcover classification. Widely known pixel-based and object-based image analysis methods have been implemented in this work like Maximum Likelihood, Support Vector Machine, k-Nearest Neighbor, Feature Analyst and Sub-pixel. Classification results from Landsat OLI provide more accurate results comparing to the Landsat TM. Object-based classifications produced a more uniform result, but suffer from the absorption of small rare classes into large homogenous areas, as a consequence of the segmentation, merging and the spatial parameters in the spatial resolution (30 m) of Landsat images. Based exclusively on the overall accuracy reports, the SVM pixel-based classification from Landsat 8 proved to be the most accurate for the purpose of mapping urban land cover, using medium spatial resolution imagery.     

Evaluation of the RPC Model for Ziyuan-3 Three-line Array Imagery

Ziyuan-3 (ZY-3) satellite is the first civilian stereo mapping satellite in China and was designed to achieve the 1: 50,000 scale mapping for land and ocean. Rigorous sensor model (RSM) is required to build the relationship between the three-dimensional (3D) object space and two-dimensional (2D) image space of ZY-3 satellite imagery. However, each satellite sensor has its own imaging system with different physical sensor models, which increase the difficulty of geometric integration of multi-source images with different sensor models. Therefore, it is critical to generate generic model, especially rational polynomial coefficients (RPCs) of optical imagery. Recently, relatively a few researches have been conducted on RPCs generation to ZY-3 satellite. This paper proposes an approach to evaluate the performance of RPCs generation from RSM of ZY-3 imagery. Three scenario experiments with different terrain features (such as ocean, hill, city and grassland) are designed and conducted to comprehensively evaluate the replacement accuracies of this approach and analyze the RPCs fitting error. All the experimental results demonstrate that the proposed method achieved the encouraging accuracy of better than 1.946E?04 pixel in both x-axis direction and y-axis direction, and it indicates that the RPCs are suitable for ZY-3 imagery and can be used as a replacement for the RSM of ZY-3 imagery.     

Sentinel-2A与Landsat 8O LI逐像元辐射归一化方法研究

考虑不同传感器光谱响应函数差异及不同地物类型反射率光谱的差异，提出了一种逐像元辐射归一化方法，并以2017年7月17日内蒙古达里诺尔湖地区准同步过境的Sentinel-2A及Landsat 8数据为例，对两类数据可见-近红外波段（VNIR）地表反射率结果进行归一化。首先采用Sen2cor方法及NASA官方提供大气校正算法，分别对Sentinel-2A及Landsat 8 OLI影像进行大气校正并重采样到同一空间分辨率；然后基于光谱库计算匹配因子并构建图像与光谱库之间的匹配转换模型，实现像元尺度上从Sentinel-2影像到Landsat 8影像地表反射率相似波段之间的转换。结果表明，经逐像元归一化的影像相比原始影像及经HLS光谱归一化的影像，与Landsat 8 VNIR波段的相关性明显提高，辐射一致性增强。该转换模型为多源中高分辨率遥感图像高精度辐射归一化提供了新思路。     

资源三号卫星成像在轨几何定标的探元指向角法

从资源三号卫星（ZY-3）影像严格几何模型出发,通过对星敏感器坐标系下各CCD探元指向角的分析,提出一种ZY-3成像在轨几何定标的探元指向角法。经对嵩山和洛阳两个试验区     

Landsat系列卫星对地观测40年回顾及LDCM前瞻

Landsat系列卫星数据凭借其长期连续、全球覆盖、适中的时间空间分辨率和科学的数据存档与分发策略等优势,逐渐成为地表特征和地球系统科学研究中最有效的遥感数据之一,并广泛应用于生态环境、农林地矿、能源资源、教育科研和政府管理等领域。而第8代陆地卫星--陆地卫星数据连续任务卫星(LDCM)于2013年2月发射升空,该卫星携带了运行性陆地成像仪(OLI)和热红外传感器(TIRS)两种传感器。与Landsat 7/ETM+相比,OLI/TIRS在波段设置、辐射分辨性能和扫描方式上都得到很大改进,其中OLI共包括9个波段,新增海岸带(coastal)监测和卷云(cirrus)识别波段,TIRS则设置了两个热红外波段。如果LDCM能够成功升空运行,它将继续承担起长期连续对地观测的使命。     

利用Sentinel-2A多光谱成像仪与Landsat8陆地成像仪影像进行普陀山岛植被分类效果比较

卫星遥感技术可用于海岛资源调查。Sentinel-2A与Landsat 8两颗卫星都可免费提供空间分辨率较高的多光谱遥感影像，在海岛调查中的应用潜力较大。本文以浙江舟山普陀山岛为例开展了针对这两种影像在海岛植被分类中的应用效果的研究，分别利用Sentinel-2A多光谱成像仪（MSI）和Landsat 8陆地成像仪（OLI）影像基于最大似然法分类获得了该岛阔叶林、针阔混交林、针叶林、灌丛、草丛等植被及其他地物的分布情况，并进行了精度检验，结果表明MSI的总体分类精度略高于OLI。     

应用时间序列EVI的MERSI多光谱混合像元分解

针对风云3数据的特点,本文将EVI生长曲线引入多光谱混合像元的分解。首先,利用Landsat8 OLI影像,采用支持向量机的分类方法,提取研究区域的耕地信息,利用该信息对风云MERSI数据进行掩膜处理,获得研究区域的耕地影像。接着,利用MERSI时序影像,计算像元EVI值,通过SG滤波,构建农作物(端元)和混合像元的EVI生长曲线。通过实地调查,获取研究区的农作物端元,尤其对主要的农作物玉米,在空间上均匀选取了14个端元。然后,采用传统的方法,将14种玉米端元生长曲线分别与其它端元组合,进行混合像元分解。发现分解的效果差异很大,提取的玉米种植面积从191.90 km2到574.83 km2不等。为提高分解精度,借用光谱匹配(光谱夹角最小)的方法(用生长曲线代替光谱曲线)自适应选择与混合像元EVI曲线最相似的玉米端元作为组合端元,进行混合像元分解。结果得到玉米的种植面积为589.95 km2,比传统方法的最好(相对)精度提高了2%。