CHRIS/PROBA数据条带噪声去除方法比较

PROBA是欧空局于2001年发射的一颗最小的对地观测卫星,CHRIS是搭载于PROBA平台上的最主要的成像光谱分光计,具有五个成像模式,以其卓越的光谱空间分辨率及多角度的优势为着不同的研究目的分别对陆地、海洋及内陆水体进行成像。但CHRIS/PROBA数据本身被严重的条带噪声所污染,尤其是垂直条带噪声,因而去除条带噪声则成为使用CHRIS数据的首要与处理工作。本文描述了几种去除条带方法,包括传统的矩匹配方法,新的迭代辐射纠正方法,校正因子法及比值法。由于这些方法基于不同的理念,各个方法的去噪效果必定有所差异,本文目的是通过比较选出其中最好的一种方法。每种方法的结果从两方面进行了比较、评定:定性的视觉效果及图像变换前后的定量的灰度偏移值,比较结果列成表格,从而挑选出最优秀的一种方法。     

一种基于最小二乘估计的深度图像曲面拟合方法

1　引言深度图像数据直接反映了景物表面的三维几何信息 ,因而得到了计算机视觉研究人员的关注 .在深度图像的三维表面分析中 ,包含有丰富几何形状信息的表面局部特征 ,如法向矢量 ,高斯曲率等 ,对于分析和描述三维表面特性非常重要 .用模板法可以提取这些特征 ,但其抗噪声能力差[1 ] .曲面拟合是另一条思路 .通过拟合三维表面再计算表面几何特征 .文献[2 ,3 ]研究了两种方法拟合三维表面 ,抗噪声能力也不好 .一般来说 ,深度图像数据根据等角测距原理获得 ,因而不是等网格间距深度数据 .美国Perceptron公司提供了一种深度数据 :非等网格深…     

一种高光谱图像条带噪声去除改进算法

传统的矩匹配方法改变了图像在成像行或列方向的均值分布,使原始图像信息发生了较大改变。在分析HJ-1-A星超光谱图像条带噪声的基础上,提出了一种改进的矩匹配方法,将传统矩匹配算法中参考图像的平均值和标准差分别用平滑滤波处理后的列均值和方差来代替。实验结果表明,与传统矩匹配方法相比,该方法能减少图像信息的丢失,并能在保持原始图像特征的前提下有效地去除条带噪声。这种方法在其它多传感器遥感图像的条带噪声去除中也有很强的适用性。     

一种基于平面拟合的图像恢复方法

论述了在进行实际图像处理时所使用的一种对于不均匀光照图像进行恢复的方法。该方法基于平面拟合来估算出实际的光照强度分布,并根据图像各点的反射系数不变这一特性来对图像进行修正。实验结果表明,该方法有效地克服了不均匀光照所造成的对于图像处理的不利影响。     

一种改进微小卫星伪码测距信噪比的有效方法

微小卫星上的星载测控应答机最基本的功能是接收、解调并转发地面战发来的伪码测距信号,供地面站测距.微小卫星体积小、重量轻、功耗低的特点导致测控应答机工作功率较低和天线增益较小,测距伪码往往淹没在嘈杂的噪声中.严重影响后续信号同步信号提取、解调和检测,降低测距精度,甚至导致无法测距.传统基于功率谱带宽的滤波器只能起到一定程度提高信噪比的作用,本文提出基于最小二乘法问题的最小范数准则进行波形拟合的多项式滤波器方法,能够进一步显著提高信噪比,解决强干扰下伪码信号提取的问题.     

基于小波变换的图像条带噪声去除方法

条带噪声的存在不但妨碍高光谱图像的目视判读,而且制约高光谱遥感的定量应用。针对小波变换法条带噪声去除过程中遇到的条带噪声和图像有用信息难以有效分离的问题,根据小波变换的方向性和数学显微镜特性,提出了一种新的基于小波变换的条带噪声去除方法。这种方法首先对含有条带噪声的图像进行一定层数的小波分解;然后对每一层分解得到的与条带噪声分布方向相同的子图像再进行一定层数的小波分解,从而实现条带噪声和图像有用信息的有效分离,将含有条带噪声的子图像置零;最后利用小波反变换得到去除条带噪声的图像。以欧洲空间局PROBA卫星上搭载的CHRIS高光谱数据为例,采用相关系数(R)、结构相似度(SSIM)和峰值信噪比(PSNR)3个定量指标,对比分析了新方法与矩匹配法、傅立叶滤波法和小波阈值法的条带噪声去除效果。结果表明新方法去噪后的图像具有最高的R、SSIM和PSNR,新方法能够有效地去除高光谱图像中的条带噪声,同时较好地保留了原始图像的有用信息。
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实现图标在曲线上平滑移动的一种方法

本文主要探讨了对拟合曲线轨迹上移动的物体,如何实时计算物体在当前坐标点的偏转角度,以保证物体平滑移动的算法,以及如何将其应用于动画系统中。     

一种改进的中文文档图像倾斜检测方法

图像获取设备将纸质文档转换为文档图像时,经常会使文档图像出现某种程度的倾斜,从而可能使后续的文档版面理解和OCR识别算法失败。文中提出一种基于近邻法的中文图像的倾斜角度检测方法,并采用最小二乘法减小倾斜估计的误差,从而大大优化了运算速度,增强了算法的鲁棒性,与现有方法相比,具有运算速度快,检测精度高的优势。算法在Visual C＋＋下编程加以实现,通过对检测库中100幅倾斜中文文档图像的检测证明,该方法具有精度高和适应性强的特点。     

一种改进的字符图像分割方法

针对文本图像中字符图像分割问题,该文介绍了一种基于局部阈值分割的LLT算法,给出了相应的数学模型,然后进一步分析了算法的关键参数设置,并给出了相应的改进方法。实验结果证明,该文方法不仅可以有效的降低背景噪声的影响,还对具有不同字符宽度的文本图像分割具有很强的适应性。     

基于行间留白的文档图像校正方法

基于2D图像处理的文档图像校正技术通常先进行单词或字符分割,会导致算法复杂、耗时。针对该问题,提出一种改进的校正算法。运用向上优先区域生长法搜索行间留白,实现行间分割得到弯曲行图像。对弯曲行图像采用改进二遍扫描法标记字符四连通区域获得行基点,对行基点使用最小二乘法拟合三次多项式,并将多项式曲线拉直拼接出全图。OCR应用和对比实验结果表明,该算法可提高字符识别率,并且能得到较好的视觉效果。     

基于CHRIS/PROBA数据和地面实测光谱的三峡库区水体叶绿素a浓度反演研究

使用欧空局2001年发射的小卫星PROBA所获取的CHRIS数据,结合卫星过境时的准实时地面试验数据分析,提出了一种新的半经验模型用于反演三峡库区的叶绿素a.反演的最大误差为33%,最小误差8%,平均误差22%,均在预计的范围内.考虑到实验数据与卫星过境具有6-7天的时延,可基本认为反演结果与实测值符合,并反映了叶绿素a的全局分布,反演精度满足实用要求.     

Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations

Neural networks trained over radiative transfer simulations constitute the basis of several operational algorithms to estimate canopy biophysical variables from satellite reflectance measurements. However, only little attention was paid to the training process which has a major impact on retrieval performances. This study focused on the several modalities of the training process within neural network estimation of LAI, FCOVER and FAPAR biophysical variables. Performances were evaluated over both actual experimental observations and model simulations. The SAIL and PROSPECT radiative transfer models were used here to simulate the training and the synthetic test datasets. Measurements of LAI, FCOVER and FAPAR were achieved over the Barrax (Spain) agricultural site for a range of crop types concurrently to CHRIS/PROBA satellite image acquisition. Results showed that the spectral band selection was specific to LAI, FCOVER and FAPAR variables. The optimal band set provided significantly improved performances for LAI, while only small differences were observed for the other variables. Gaussian distributions of the radiative transfer model input variables performed better than uniform distributions for which no prior information was exploited. Including moderate uncertainties in the reflectance simulations used in the training process improved the flexibility of the neural network in cases where simulations departed slightly from observations. Simple neural network architecture with a single hidden layer of five tangent sigmoid transfer functions was performing as good as more complex architectures if the training dataset was larger than ten times the number of coefficients to tune. Small sensitivity of performances was observed depending on the way the solution was selected when several networks were trained in parallel. Finally, comparison with a NDVI based approach showed the generally better retrieval accuracy of neural networks.     

CHRIS 高光谱图像森林类型分类方法比较研究

以长白山为试验区,选择CHRIS/PROBA高光谱零度角遥感数据,在对其进行预处理的基础上,通过应用最大似然法(MLC)、最小距离法、支持向量机法(SVM)和光谱角填图法(SAM)等几种常用的高光谱遥感分类方法对影像进行森林类型分类。利用混淆矩阵对分类结果进行验证,结果显示:在高光谱遥感森林类型分类中,SVM总体分类精度最高,为84.60％;其次是MLC,为 83.53％,最小距离法73.81％,SAM 56.49％。Kappa系数从高到底为:SVM 0.78,MLC 0.77,最小距离法0.68,SAM 0.52。经过比较分析,得出SVM分类方法精度最高,这表明该方法用于高光谱遥感森林分类中的实用性和优越性。     

Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations

This study focuses on the potential of satellite hyperspectral imagery to monitor vegetation biophysical and biochemical characteristics through narrow-band indices and different viewing angles. Hyperspectral images of the CHRIS/PROBA sensor in imaging mode 1 (5 observation angles, 62 bands, 410-1005 nm) were acquired throughout a two-year period for a Mediterranean ecosystem fully covered by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident ecophysiological field measurements were conducted. Leaf area index (LAI), leaf biochemical content (chlorophyll a, chlorophyll b, carotenoids) and leaf water potential were measured. The hyperspectral images were corrected for coherent noises, cloud and atmosphere, in order to produce ground reflectance images. The reflectance spectrum of each image was used to calculate a variety of vegetation indices (VIs) that are already published in relevant literature. Additionally, all combinations of the 62 bands were used in order to calculate Normalized Difference Spectral Indices (NDSI_(x,y)) and Simple Subtraction Indices (SSI_(x,y)). The above indices along with raw reflectance and reflectance derivatives were examined for linear relationship with the ground-measured variables and the strongest relationships were determined. It is concluded that higher observation angles are better for the extraction of biochemical indices. The first derivative of the reflectance spectra proved to be very useful in the prediction of all measured variables. In many cases, complex and improved spectral indices that are proposed in the literature do not seem to be more accurate than simple NDSIs such as NDVI. Even traditional broadband NDVI is proved to be adequate in LAI prediction, while green bands seem also very useful. However, in biochemical estimation narrow bands are necessary. Indices that incorporate red, blue and IR bands, such as PSRI, SIPI and mNDVI presented good performance in chlorophyll estimation, while CRI did not show any relevance to carotenoids and WI was poorly correlated to water potential. Moreover, analyses indicated that it is very important to use a near red-edge band (701 nm) for effective chlorophyll index design. SSIs that incorporate 701 nm with 511 or 605 nm showed best performance in chlorophyll determination. For carotenoid estimation, a band on the edge of carotenoid absorption (511 nm) combined with a red band performed best, while a normalized index of two water absorption bands (945, 971 nm) proved to be an effective water index. Finally, the attempt to investigate stress conditions through pigment ratios resulted in the use of the band centred at 701 nm.     

Band depth analysis of CHRIS/PROBA data for the study of a Mediterranean natural ecosystem. Correlations with leaf optical properties and ecophysiological parameters

The absorption feature approach was used in CHRIS multiangular hyperspectral data in order to investigate its potential for ecosystem remote sensing. For that purpose, CHRIS images in mode 1 were acquired throughout a two-year period for a Mediterranean ecosystem dominated by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident in situ Leaf spectra and ecophysiological measurements (Leaf Area Index, leaf pigment content and leaf water potential) were conducted. After data preprocessing, absorption feature information was calculated for both CHRIS and Leaf spectra for the whole spectrum. Three common characteristic absorption features within the spectral areas 450-550 nm, 550-750 nm and 900-1000 nm were detected. Each spectral area was then examined separately and four characteristic parameters were calculated that described the pattern, magnitude and position of the maximum absorption. Correlations between CHRIS and Leaf spectra for each date and viewing angle (VA) were then conducted. All correlations, either on full continuum removed spectra or on spectral areas, showed high coefficients of determination, especially (i) in higher observation angles (VA + 55), (ii) during the wet season and (iii) in strong absorptions such as the “red absorption”. Subsequently, correlations between CHRIS and Leaf absorption parameters of selected spectral areas with field-measured ecophysiological parameters were examined. Ecophysiological parameters proved to be highly correlated to CHRIS and Leaf absorption parameters in magnitude and/or pattern of the absorption feature and less in wavelength of the maximum absorption. CHRIS VAs +/− 36 showed the highest correlations although the type of relation, linear or nonlinear, was not conclusive. Finally, a first comparison between narrowband spectral indices and absorption features in correlations with ecophysiological parameters showed that both methods provide significant and comparable results, with oblique angles showing best performance. However, ecophysiological parameters are generally better predicted linearly by narrowband spectral indices issued from CHRIS, with most significant differences appearing on pigments absorbing mainly within 450-550 nm.     

Atmospheric correction of PROBA/CHRIS data in an urban environment

The Compact High Resolution Imaging Spectrometer (CHRIS) is an imaging spectrometer onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) satellite. However, it has been shown that CHRIS presents some miscalibration trends over the spectral region covered. This paper reports a practical procedure for the atmospheric correction of CHRIS images based on field recalibration in an urban environment. In the first stage, the spectra of surface targets are measured and used to simulate the spectral radiance at the top of the atmosphere (TOA) for each channel and to determine the recalibration coefficients of the CHRIS images. In the second stage, two methods for atmospheric correction are examined: the radiative transfer model (RTM) and the improved dark-object subtraction (IDOS) method. For comparison purposes, the empirical line method (ELM) is also evaluated. The accuracy assessment shows that the RTM with the Moderate Resolution Transmittance (MODTRAN) code provides the most accurate atmospheric correction for the multiangular CHRIS images when using the proposed procedure.     

Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data

View angle effects present in spectral vegetation indices can either be regarded as an added source of uncertainty for variable retrieval or as a source of additional information, enhancing the variable retrieval; however, the magnitude of these angular effects remains for most indices unknown or unquantified. We use the ESA-mission CHRIS-PROBA (Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy) providing spaceborne imaging spectrometer and multiangular data to assess the reflectance anisotropy of broadband as well as recently developed narrowband indices. Multiangular variability of Hemispherical Directional Reflectance Factor (HDRF) is a prime factor determining the indices´ angular response. Two contrasting structural vegetation types, pine forest and meadow, were selected to study the effect of reflectance anisotropy on the angular response. Calculated indices were standardized and statistically evaluated for their varying HDRF. Additionally we employ a coupled radiative transfer model (PROSPECT/FLIGHT) to quantify and substantiate the findings beyond an incidental case study. Nearly all tested indices manifested a prominent anisotropic behaviour. Apart from the conventional broadband greenness indices [e.g. Simple Ratio Index (SRI), Normalized Difference Vegetation Index (NDVI)], light use efficiency and leaf pigment indices [e.g. Structure Insensitive Pigment Index (SIPI), Photochemical Reflectance Index (PRI) and Anthocyanin Reflectance Index (ARI)] did express significant different angular responses depending on the vegetation type. Following the quantification of the impact, we conclude that the angular-dependent fraction of non-photosynthetic material is of critical importance shaping the angular signature of these VIs. This work highlights the influence of viewing geometry and surface reflectance anisotropy, particularly when using light use efficiency and leaf pigment indices.     

Inversion of a coupled canopy-atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study

Since the launch of sensors with angular observation capabilities, such as CHRIS and MISR, the additional potential of multi-angular observations for vegetation structural and biochemical variables has been widely recognised. Various methods have been successfully implemented to estimate forest biochemical and biophysical variables from atmospherically-corrected multi-angular data, but the use of physically based radiative transfer (RT) models is still limited. Because both canopy and atmosphere have an anisotropic behaviour, it is important to understand the multi-angular signal measured by the sensor at the top of the atmosphere (TOA). Coupled canopy-atmosphere RT models allow linking surface variables directly to the TOA radiance measured by the sensor and are therefore very interesting tools to use for estimating forest variables from multi-angular data.We investigated the potential of TOA multi-angular radiance data for estimating forest variables by inverting a coupled canopy-atmosphere physical RT model. The case study focussed on three Norway spruce stands located at the Bily Kriz experimental site (Czech Republic), for which multi-angular CHRIS and field data were acquired in September 2006. The soil-leaf-canopy RT model SLC and the atmospheric model MODTRAN4 were coupled using a method allowing to make full use of the four canopy angular reflectance components provided by SLC. The TOA radiance simulations were in good agreement with the spectral and angular signatures measured by CHRIS. Singular value decompositions of the Jacobian matrices showed that the dimensionality of the variable estimation problem increased from 3 to 6 when increasing the number of observation angles from 1 to 4. The model inversion was conducted for two cases: 4 and 7 variables. The most influential parameters were chosen as free variables in the look-up tables, namely: vertical crown cover (Cv), fraction of bark material (fB), needle chlorophyll content (needleCab), needle dry matter content (needleCdm) for the 4-variable case, and additionally, tree shape factor (Zeta), dissociation factor (D), and needle brown pigments content (needleCs) in the 7-variable case. All angular combinations were tested, and the best estimates were obtained with combinations using two or three angles, depending on the number of variables and on the stand used. Overall, this case study showed that, although making use of its full potential is still a challenge, TOA multi-angular radiance data do have a higher potential for variable estimation than mono-angular data.     

Multi-angular reflectance properties of a hemiboreal forest: An analysis using CHRIS PROBA data

Forest types differ in their hyperspectral anisotropy patterns mainly due to species-specific geometrical structure, spatial arrangement of canopies and subsequent shadow patterns. This paper examines the multi-angular, hyperspectral reflectance properties of typical hemiboreal forests during summer time using three simultaneous CHRIS PROBA (mode 3) scenes and stand inventory data from the Järvselja Training and Experimental Forestry District in southeastern Estonia. We investigated the magnitude and reasons for the differences in the anisotropy patterns of deciduous and coniferous stands at three backward viewing angles. A forest reflectance model (FRT) was used as a tool to provide a theoretical basis to the discussion, and to estimate the directional contribution of scattering from crowns and ground to total stand reflectance for the two forest types. The FRT model simulated successfully the HDRF (hemispherical–directional reflectance factor) curves of the study stands to match those obtained from the CHRIS image, yet it produced a smaller and less wavelength-dependent angular reflectance effect than was observed in the satellite image. The main results of this study provide new information for separating the spectral contribution of the forest floor (or understory layer) from the tree canopy layer: (1) the red edge domain was identified to have the largest contribution from forest understory, and (2) the more oblique the viewing angle, the smaller the contribution from the understory. In addition, coniferous stands were observed to have a specific angular effect at the red and red edge domain, possibly as a result of the hierarchical structure and arrangement of coniferous canopies.