一种稳健的多时相遥感图像相对辐射校正方法

变化检测是通过分析多时相遥感图像实现土地利用动态监视的一种有效方法，但在变化检测 分析前，需要经过辐射校正消除光照等因素对地物光谱辐射的影响，使同一地物在不同时相 影像中具有相同的辐射量。根据地物在不同时相遥感图像中的光谱特性满足线性关系的 特点，提出一种自动实现多时遥感图像相对辐射校正的稳健方法，首先通过最小差分回归找 出非变化地物在多时相遥感图像中的辐射关系 |然后利用变化区域证实过程消除变化区域对 辐射校正处理的影响 |最后通过循环迭代实现图像间的辐射校正。提出的方法不仅可以自动 地实现多时相遥感图像的相对辐射校正，而且能够保证图像的辐射分辨率不会因为辐射校正而降低。     

Colour balancing of satellite imagery based on a colour reference library

Generating mosaics of images obtained at different times is a challenging task because of the radiometric differences between the adjacent images introduced by the solar incident angle, atmosphere, and illumination condition. For most of the existing colour-balancing methods, the standard for determining the reference image is not unified, thus yielding different calibration results. Besides, traditional methods may suffer from colour error propagation and the two-body problems. A novel colour-balancing method for satellite imagery based on a colour reference library is proposed in this article, which aims to eliminate the effect of colour difference between different images for visually appealing and seamless image mosaicking. The proposed method contains two parts: the establishment of a colour reference library and the colour-balancing method based on it. Colour reference library is a database storing colour and other related information from the existing mosaic imagery. The colour information of the existing mosaic imagery is visually appealing and consistent with human visual perception. By automatically selecting appropriate colour reference information from the colour reference library according to the geographical scope and acquisition season information of the target images, the proposed approach provides effective solutions for choosing suitable reference image, colour error propagation, and the two-body problem in traditional colour-balancing methods. Experimental results demonstrate that the proposed approach performs well in the colour-balancing process.     

基于Terra/MODIS数据的HJ-1B/CCD1交叉定标方法研究

交叉辐射定标是国际上新近发展起来的一种无场地定标方法,它的应用弥补了场地定标成本较高、定标参数更新周期较长的不足。对于我国2008年发射的环境与灾害监测预报小卫星CCD数据而言,探索交叉辐射定标方法的适用性,对及时发现传感器辐射性能的变化,促进CCD遥感数据的定量化应用具有重要意义。本研究以辐射定标精度较高的Terra/MODIS数据为参考,分别使用光线匹配法（RM）和辐射传输模型方法（RTM）对HJ-1B/CCD1数据进行交叉辐射定标,并与相同条件下进行的场地定标结果比较。实验结果表明,使用这两种方法获取的CCD1的第2、3、4波段的定标结果与场地定标结果差异较小,只有第1波段定标结果与场地定标结果差异相对较大,这证明了交叉辐射定标方法的有效性。另外,虽然RTM方法考虑了参考传感器和待定标传感器光谱响应和观测几何的差异,但是由于RTM方法会受到所使用的6S模型本身的误差以及输入的大气参数、地表参数测量误差的影响,该方法并不总是优于RM方法。     

基于时间序列的MODIS遥感数据的辐射定标

针对基于统计量的辐射相对定标的不足之处,提出了一种基于小波提升分解的小波变换算法,以解决时间序列上的MODIS遥感图像的辐射定标问题。该方法使用不同年限获取的MODIS卫星影像分别进行提升小波分解,对低频信号进行灰度辐射定标后再与高频信号重构,通过提升小波逆变换得到结果图像,实验结果表明效果较好。     

基于匹配与平差的景象镶嵌方法

在概括介绍消附相邻景象辐射差异常用算法的基础上,为有效消除相邻景象间几何位置的差异,提出了一种将影象匹配与平差理论相结合的影象镶嵌新算法,该方法是在镶嵌影象的重叠部分进行基于特征的影象匹配,即首先获取一序列同名点,且这些同名点的匹配精度在一个象元以内;然后用正形多项式模拟这些同名点间的几何位置差异,再按最小二乘法原理对右边影象进行平差改正,以实现两影象空间位置上的无缝连接。实验结果表明,庐镶嵌方法具有良好的效果。     

Landsat-7 ETM+ radiometric normalization comparison for northern mapping applications

Relative radiometric normalization has long been performed to generate consistency among individual Landsat scenes for production of composites containing multiple scenes. Normalization methods have relied on matching identical and assumed invariant features in both images of an overlapping pair, or on invariant targets that are not necessarily the same features. Problems with overlap normalization methods include sensitivity to outliers in overlap data caused by atmospheric or land cover change between scenes, which can lead to radiometric error propagation across a mosaic caused by a normalized scene becoming a reference for the subsequent scene entered into the mosaic. Solutions to such problems include interactive outlier removal to generate a normalization function using a ‘no change’ data set and methods that are robust against outliers to automatically generate normalization functions with minimal user input. This paper compares two normalization methods that use a robust regression technique called Theil-Sen with an established overlap normalization method. The first method uses Theil-Sen regression to generate a normalization function between overlap regions, while the second uses Theil-Sen to normalize to coarse-resolution composite reflectance data from the SPOT VEGETATION (VGT) sensor. The results of the normalizations were evaluated in two ways: (1) using statistics generated between overlap regions; and (2) separately using coarse-resolution data as a reference. Both overlap normalization methods performed almost identically; however, Theil-Sen was faster and easier to implement than its traditional counterpart due to its insensitivity to outliers and capability for full automation. While overlap and coarse-resolution normalizations each outperformed the other when evaluated against its calibration set, error propagation caused by outliers in overlap samples was avoided in the normalization to coarse-resolution imagery. Advantages offered by normalization to coarse-resolution data using robust regression, including full automation, make this method particularly attractive for generation of large area mosaics containing 100 Landsat scenes or more.     

Kruppa方程与摄像机自标定

首先研究探讨了基于绝对二次曲线(the absolute conic)进行摄像机自标定鲁棒性差的 内在原因.研究发现,该类方法鲁棒性不足的原因主要有三个方面:1)在目标函数的全局最小点 处存在大范围的平坦区域,使得任何数值优化算法难以达到全局最小点;2)当存在噪声时,上 述平坦区域内会出现大量局部极小值,这样数值优化算法就非常容易收敛到靠近初值的局部极 小值,使得算法对初始值的选取十分敏感;3)当有噪声时,目标函数的全局最小值极易偏离正 确值.这样,即使数值算法找到了全局最小值,该最小值也不再对应正确的摄像机内参数值.鉴 于上述情况,探讨了如何通过平面场景来确定内参数矩阵的初始值,而后进一步利用Kruppa方 程的约束来精化内参数矩阵的二步式方法.     

Extension of retrospective datasets using multiple sensors. An approach to radiometric intercalibration of Landsat TM and MSS data

The establishment of monitoring frameworks for environmental problems is frequently based on retrospective, multi-temporal series of satellite images. The derivation of concise conclusions from these series largely depends on their quantitative consistency; hence, a completely standardized analysis of multi-temporal time series is mandatory. In the context of the required radiometric rectification, sensor calibration is an essential component, the accuracy of which largely determines the overall result. While for newer sensor systems extensive documentation on the development of the sensor's sensitivity exists, older systems often lack the corresponding information.A methodology has been developed, tested and validated to radiometrically intercalibrate different sensor systems based on the precondition of simultaneous image acquisition, which is for example provided by the Landsat TM and MSS sensor systems hosted on Landsat 5. Different interpolation and processing steps within the procedure have been analyzed, and an intercalibration scheme is proposed to derive Landsat MSS calibration factors from Landsat TM, which is used as a calibrated reference. It supports a full radiometric rectification of Landsat MSS data and enables their incorporation into satellite image time series, which can thus be significantly extended by a maximum of 12 years.     

IMRAN: a noise estimation method for relative radiometric calibration data

Radiometric calibration is the foundation for remote sensors to accurately record the reflected energy from targets and to also effectively display the reflectance diversity among them. As one of the calibration methods, pre-launch laboratory relative calibration is essentially a normalizing process for each detector of a sensor at different intensity levels of various radiation sources. However, interferences such as stray light, dark current, and stochastic noise will cause some deviation of the normalizing correction factor. In this article, we propose an integral noise (a combination of the aforementioned three noises) estimation method based on the correlation between the elements of the calibration data itself. Abbreviated as IMRAN (Iterative Maximal Residual As Noise), this method is an iteration procedure using least square fitting to calculate the maximum residual of the sensor pixel in question against the rest sensor pixels and to consider this value as the estimated noise. The iteration is continued after subtracting the noise from the raw data of the sensor pixel until the noise estimation gets converged and then the accumulation of the results from each round is the final estimated noise. And this procedure is applied to every sensor pixel. The verification results demonstrated the IMRAN method can effectively estimate the integral noise of pre-launch radiometric calibration data and substantially improve its precision. When the number of radiation level increases, the precision of the estimated noise will be rapidly increased, whereas the number of sensor pixels has no obvious effect. Because this IMRAN method uses the data of every sensor pixel, it is sensitive to the outlier, which can be eliminated by variance detection as part of the IMRAN method.     

New maximum likelihood motion estimation schemes for noisy ultrasound images

When performing block-matching based motion estimation with the ML estimator, one would try to match blocks from the two images, within a predefined search area. The estimated motion vector is that which maximizes a likelihood function, formulated according to the image formation model. Two new maximum likelihood motion estimation schemes for ultrasound images are presented. The new likelihood functions are based on the assumption that both images are contaminated by a Rayleigh distributed multiplicative noise. The new approach enables motion estimation in cases where a noiseless reference image is not available. Experimental results show a motion estimation improvement with regards to other known ML estimation methods.     

高分辨率SAR辐射定标中两种角反射器响应测量方法的应用对比研究

利用角反射器进行SAR图像辐射定标是一种简单、实用、精度较高的绝对定标方法。其关键技术包括角反射器响应测量、天线方向图拟合和定标常数确定。针对机载分米级高分SAR影像定标问题,在同一场景中布设了30和10cm两组三面角反射器,依据雷达中心入射角估计值、均方根误差和变异系数等指标,分析了已有的两种角反射器响应测量方法,即峰值法和积分法的适用性,并提出了一种新的天线方向图拟合方法。最后给出了两种方法对高分辨率SAR辐射定标的适用性结论。     

Robust radiometric calibration and vignetting correction

In many computer vision systems, it is assumed that the image brightness of a point directly reflects the scene radiance of the point. However, the assumption does not hold in most cases due to nonlinear camera response function, exposure changes, and vignetting. The effects of these factors are most visible in image mosaics and textures of 3D models where colors look inconsistent and notable boundaries exist. In this paper, we propose a full radiometric calibration algorithm that includes robust estimation of the radiometric response function, exposures, and vignetting. By decoupling the effect of vignetting from the response function estimation, we approach each process in a manner that is robust to noise and outliers. We verify our algorithm with both synthetic and real data which shows significant improvement compared to existing methods. We apply our estimation results to radiometrically align images for seamless mosaics and 3D model textures. We also use our method to create high dynamic range (HDR) mosaics which are more representative of the scene than normal mosaics.     

Automatic Radial Distortion Estimation from a Single Image

Many computer vision algorithms rely on the assumptions of the pinhole camera model, but lens distortion with off-the-shelf cameras is usually significant enough to violate this assumption. Many methods for radial distortion estimation have been proposed, but they all have limitations. Robust automatic radial distortion estimation from a single natural image would be extremely useful for many applications, particularly those in human-made environments containing abundant lines. For example, it could be used in place of an extensive calibration procedure to get a mobile robot or quadrotor experiment up and running quickly in an indoor environment. We propose a new method for automatic radial distortion estimation based on the plumb-line approach. The method works from a single image and does not require a special calibration pattern. It is based on Fitzgibbon’s division model, robust estimation of circular arcs, and robust estimation of distortion parameters. We perform an extensive empirical study of the method on synthetic images. We include a comparative statistical analysis of how different circle fitting methods contribute to accurate distortion parameter estimation. We finally provide qualitative results on a wide variety of challenging real images. The experiments demonstrate the method’s ability to accurately identify distortion parameters and remove distortion from images.     

Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images

The homogeneity of time series of satellite images is crucial when studying abrupt or gradual changes in vegetation cover via remote sensing data. Various sources of noise affect the information received by satellites, making it difficult to differentiate the surface signal from noise and complicates attempts to obtain homogeneous time series. We compare different procedures developed to create homogeneous time series of Landsat images, including sensor calibration, atmospheric and topographic correction, and radiometric normalization. Two seasonal time series of Landsat images were created for the middle Ebro Valley (NE Spain) covering the period 1984–2007. Different processing steps were tested and the best option selected according to quantitative statistics obtained from invariant areas, simultaneous medium-resolution images, and field measurements. The optimum procedure includes cross-calibration between Landsat sensors, atmospheric correction using complex radiative transfer models, a non-lambertian topographic correction, and a relative radiometric normalization using an automatic procedure. Finally, three case studies are presented to illustrate the role of the different radiometric correction procedures when analyzing and explaining gradual and abrupt temporal changes in vegetation cover, as well as temporal variability. We have shown that to analyze different vegetation processes with Landsat data, it is necessary to accurately ensure the homogeneity of the multitemporal datasets by means of complex radiometric correction procedures. Failure to follow such a procedure may mean that the analyzed processes are non-recognizable and that the obtained results are invalid.     

用基于相对辐射校正的TM影像监测绍兴土地覆盖变化

基于绍兴市1984年和1997年的两期TM影像，用相对辐射校正方法将1997年的影像校正到1984年的辐射水平上，消除多时相遥感影像问地物的辐射差异，对两期影像分别做最大似然分类，构建覆盖转移矩阵，将分类图分析运算构建覆盖变化分类混淆矩阵，并结合土地覆盖变化的驱动因子，分析得出，绍兴土地覆盖变化主要因素是城镇的扩展及人们饮食结构的变化。     

Study on radiometric intercalibration methods for DMSP-OLS night-time light imagery

The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night-time light image sets has been successfully used in various fields to monitor temporal and spatial distributions, such as urbanization and socioeconomic activities. However, the radiometric calibration of night-time light images has long been a problem that limits the application of night-time light image sets in multi-temporal analyses. The key to an intercalibration model is to automatically extract the reference pixels with stable lights and to simultaneously remove unstable (or variant) light pixels viewed as outliers (or gross error). This paper systemically compares five weighted least squares regression (WLSR) algorithms for radiometric intercalibration to determine the method with the highest accuracy, including (1) classic methods: empirical rule (ER), Danish method (DM), and posterior variance estimation (PVE); and (2) state-of-the-art methods: random sample consensus (RANSAC) and least median of squares (LMedS). Moreover, a more objective adjusted root mean square error (RMSE) method is proposed to evaluate accuracy. Through the experiments, we systemically analyse the performance of different estimators and propose recommendations for optimizing intercalibration for specific applications. Moreover, the study reveals that gross error approximatively obeys the stochastic or expansion model in the radiometric intercalibration of DMSP-OLS image sets. Overall, LMedS works best and is proposed to intercalibrate the radiometric values of night-time light image sets.     

散焦模糊量估计的相机加权标定方法

相机标定在计算机视觉领域中有着至关重要的作用.绝大多数相机标定方法假设相机为针孔模型,且需要良好聚焦的图像来保证相机内外参估计的准确性.然而,这些条件会受到相机景深的影响.在薄透镜相机模型假设下,提出了一种加权相机标定的方法,其权重考虑了控制点的模糊量信息.首先对棋盘格标定物上的每一个角点进行散焦模糊量估计,在标定过程中,将散焦模糊量的大小作为一个权重加入到标定能量函数最小化过程中,使得标定精度得到提高.该方法简单高效,不需要额外的数码设备或者特别定做的标定物.在Intel Core i7处理器的计算机下,使用合成数据以及真实数据上进行的实验结果表明,文中方法能够有效减小重投影误差,提高张正友标定方法的标定精度.     

Camera calibration using identical objects

This paper describes a method for camera calibration using identical products. In this paper, we postulate an imaginative rigid motion between any two identical products, and the imaginative rigid motion could offer a pair of circular points. As is known, three pairs of projections of the circular points are needed to result in the closed-form solution for calibration. In our method, we obtain three pairs of projections of the circular points from only two images of three identical products, or three images of two identical products. When only two identical products are utilized, our method is almost the dual of the stereo calibration from rigid motions. A direct approach is taken here instead of the two-step process in stereo calibration. Furthermore, a better projective reconstruction could be performed from the estimation of the camera parameters to avoid the dominant projective-to-affine error in the stereo calibration. Finally, we conduct a nonlinear refinement based on the maximum likelihood estimation. The experimental results from synthetic data and real data prove our method convenient and robust to noise.     

星载光学遥感器可见近红外通道辐射定标研究进展

辐射定标是遥感定量化应用的基础。在阐述辐射定标概念,意义和方法的基础上,介绍了发射前定标、在轨星上定标和在轨替代定标的各种方法,并分析了这些方法在卫星不同阶段所起的作用。以在轨替代定标中的场地定标法、场景定标法和交叉定标法为重点,详细介绍了其中各种方法的基本原理,适用范围以及当前国内外研究现状。最后,对当前国际上辐射定标的发展趋势作出展望,并针对国内的辐射定标现状提出相关的建议,使用户能够得到更高精度的遥感数据。     

Assessment of the ASAR sensor radiometric quality in comparison to ERS‐2 and RADARSAT‐1 SAR data

The estimation of geophysical parameters from Synthetic Aperture Radar (SAR) data necessitates well‐calibrated sensors with good radiometric precision. In this paper, the radiometric calibration of the new Advanced Synthetic Aperture Radar (ASAR‐ENVISAT) sensor was assessed by comparing ASAR data with ERS‐2 and RADARSAT‐1 SAR data. By analysing the difference between radar signals of forest stands, the results show differences of varying importance between the ASAR on the one hand, and the ERS‐2 and the RADARSAT‐1 on the other. For recent data acquired at the end of 2005, the difference varies from ?0.72 to +0.72 dB, with temporal variations that can reach 1.1 dB. For older data acquired in 2003 and 2004, we observe a sharp decrease in the radar signal in the range direction, which can attain 3.5 dB. The use of revised calibration constants provided recently by the European Space Agency (ESA) significantly improves the results of the radiometric calibration, where the difference between the ASAR and the other SARs will be lower than 0.5 dB.