Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation

A recently proposed method for automatic radiometric normalization of multi- and hyperspectral imagery based on the invariance property of the Multivariate Alteration Detection (MAD) transformation and orthogonal linear regression is extended by using an iterative re-weighting scheme involving no-change probabilities. The procedure is first investigated with partly artificial data and then applied to multitemporal, multispectral satellite imagery. Substantial improvement over the previous method is obtained for scenes which exhibit a high proportion of change.     

A new model for the automatic relative radiometric normalization of multiple images with pseudo-invariant features

Relative radiometric normalization (RRN) with multi-sensor images is required for land-cover change detection. However, there are only a few RRN studies using multiple sensors. This article presents a new method for normalizing multiple images with pseudo-invariant features (PIFs) (MIPIF), which includes automatic selection and step-by-step optimization of PIFs. The normalized difference water index (NDWI) was used to select the original PIFs, and statistical rules with iterative control were used to fix the final PIFs. The method was tested on multiple images from a single sensor and multiple sensors in four groups of experiments with different land-cover areas. The results show that the normalization coefficients exceeded 0.90 at a significance level of 0.01. For the reference and normalized subject images, the root mean squared error (RMSE) values of the PIFs were much smaller than those of the reference and original subject images. The difference histogram curves of the reference and normalized subject images in the PIF pixels had roughly narrow normal Gaussian distributions with one pick around the zero position. The results demonstrated that the MIPIF method considers the physical definition of the PIFs and is effective, stable, and applicable for multiple images from a single sensor and from multiple sensors.     

Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data

A semi-physical fusion approach that uses the MODIS BRDF/Albedo land surface characterization product and Landsat ETM+ data to predict ETM+ reflectance on the same, an antecedent, or subsequent date is presented. The method may be used for ETM+ cloud/cloud shadow and SLC-off gap filling and for relative radiometric normalization. It is demonstrated over three study sites, one in Africa and two in the U.S. (Oregon and Idaho) that were selected to encompass a range of land cover land use types and temporal variations in solar illumination, land cover, land use, and phenology. Specifically, the 30 m ETM+ spectral reflectance is predicted for a desired date as the product of observed ETM+ reflectance and the ratio of the 500 m surface reflectance modeled using the MODIS BRDF spectral model parameters and the sun-sensor geometry on the predicted and observed Landsat dates. The difference between the predicted and observed ETM+ reflectance (prediction residual) is compared with the difference between the ETM+ reflectance observed on the two dates (temporal residual) and with respect to the MODIS BRDF model parameter quality. For all three scenes, and all but the shortest wavelength band, the mean prediction residual is smaller than the mean temporal residual, by up to a factor of three. The accuracy is typically higher at ETM+ pixel locations where the MODIS BRDF model parameters are derived using the best quality inversions. The method is most accurate for the ETM+ near-infrared (NIR) band; mean NIR prediction residuals are 9%, 12% and 14% of the mean NIR scene reflectance of the African, Oregon and Idaho sites respectively. The developed fusion approach may be applied to any high spatial resolution satellite data, does not require any tuning parameters and so may be automated, is applied on a per-pixel basis and is unaffected by the presence of missing or contaminated neighboring Landsat pixels, accommodates for temporal variations due to surface changes (e.g., phenological, land cover/land use variations) observable at the 500 m MODIS BRDF/Albedo product resolution, and allows for future improvements through BRDF model refinement and error assessment.     

基于相对变换的半监督分类算法

为了增强基于图的局部和全部一致性(LGC)半监督算法的处理稀疏和噪声数据的能力,提出了一种基于相对变换的LGC算法。该算法通过相对变换将原始数据空间转换到相对空间,在相对空间中噪声和孤立点远离正常点,稀疏的数据变得相对密集,从而可以提高算法的性能。仿真实验结果表明,基于相对变换的LGC算法有更强的处理稀疏和噪声数据的能力。     

A comparative study on radiometric normalization using high resolution satellite images

Remotely sensed multitemporal, multisensor data are often required in Earth observation applications. A common problem associated with the use of multisource image data is the grey value differences caused by non‐surface factors such as different illumination, atmospheric, or sensor conditions. Such differences make it difficult to compare images using the same colour metric system. Image normalization is required to reduce the radiometric influences caused by non‐surface factors and to ensure that the grey value differences between temporal images reflect actual changes on the surface of the Earth.A variety of image normalization methods, such as pseudoinvariant features (PIF), dark and bright set (DB), simple regression (SR), no‐change set determined from scattergrams (NC), and histogram matching (HM), have been published in scientific journals. These methods have been tested with either Landsat TM data, MSS data or both, and test results differ from author to author. However, whether or not existing methods could be adopted for normalizing high resolution multispectral satellite images, such as IKONOS and QuickBird, is still open for discussion because of the dramatic change in spatial resolution and the difference of available multispectral bands. In this research, the existing methods are introduced and employed to normalize the radiometric difference between IKONOS and QuickBird multispectral images taken in different years. Some improvements are introduced to the existing methods to overcome problems caused by band difference and to achieve more stable and better results. The normalized results are compared in terms of visual inspection and statistical analysis. This paper examined whether or not existing methods can be directly adopted for image normalization with high resolution satellite images, and showed how these methods can be modified for use with such images.     

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.     

基于多块相对变换独立主元分析的故障诊断方法

针对复杂工业过程中故障诊断技术存在数据可分性差、噪声干扰、故障定位困难的问题,提出一种基于多块相对变换独立主元分析(MBRTICA)的故障诊断方法.为了使所提取的故障特征具有可分性,采用相对变换原理与FastICA算法融合的方式构建相对变换独立主元分析方法(RTICA)用于检测故障的发生.通过引入多块理论,将高维数据分成多个子块单元,并在每个子块单元内分别进行RTICA处理,确定故障发生的位置.最后用电主轴轴承裂纹故障的实验对所提方法进行验证,实验结果表明,基于MBRTICA的故障诊断方法可提高数据的可分性,能够有效减少噪声,同时提高故障检测的精度, 实现故障定位功能, 全面地对故障进行分析.     

SIFT特征匹配的辐射畸变图像相对校正新方法

针对常规相对辐射校正方法对未配准的主从图像对校正效果欠佳的问题,在分析了辐射畸变图像的成因与特点以及相对校正的优势之后,提出了一种基于SIFT特征匹配的相对辐射校正算法.从SIFT特征的不变性出发,分别提取主从图像对的SIFT特征并进行匹配,对匹配点的灰度值进行最小二乘回归分析,得到线性变换系数,最后通过线性变换得到校正结果.实验结果表明,此方法能够有效地减小未配准主从图像对之间的辐射差异,并在复杂光照环境下的光流场计算中得到成功的应用.     

应用相对变换的无线传感器网络分簇算法

针对无线传感器网络节点负载不均衡的问题,提出了一种应用相对变换的无线传感器网络分簇算法(RTCH)。在成簇阶段,节点将簇头剩余能量、簇头与节点和簇头与基站的传输能耗等参数利用该模型先进行相对变换,再计算簇头适宜度来选择加入簇头成簇,并通过簇头的反馈信息来控制簇的规模来优化网络性能。仿真实验结果表明,RTCH算法能更有效地均衡网络中的能量消耗,延长网络生命周期。     

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.     

On-orbit relative radiometric calibration of optical video satellites without uniform calibration sites

The rapid development of optical video satellite constellations provides a new data model for resource and environmental monitoring, and for commercial applications of satellite remote sensing. High-frequency on-orbit relative radiometric calibration is an indispensable means of improving the quality of raw frames. However, the conventional on-orbit calibration method relies on a uniform field (e.g. desert, snow, clouds, etc.), which renders it unable to perform high-frequency radiometric calibration. Here, a method of on-orbit relative radiometric calibration without uniform calibration sites for optical video satellites is proposed taking into consideration the high frame-rate characteristics of video satellites. The method can be applied using any multi-frame data, it does not require a uniform field, it is not affected by the type of field, and it can achieve near-real-time, high-frequency calibration of video satellites. Moreover, this approach is expected to increase the frequency of on-orbit radiometric calibrations while reducing the associated costs. Our results suggest that the proposed method can effectively calibrate the non-uniformity of each sensor detector, and the compensation effect is equivalent to the on-orbit calibration method based on uniform sites.     

Adaptive normalization of handwritten characters using global/localaffine transformation

This paper introduces an adaptive or category-dependent normalization method that normalizes an input pattern against each reference pattern using global/local affine transformation (GAT/LAT) in a hierarchical manner as a general deformation model. Also, the normalization criterion is clearly defined as minimization of the mean of nearest-neighbor interpoint distances between each reference pattern and a normalized input pattern. Optimal GAT/LAT is determined by iterative application of weighted least-squares fitting techniques. Experiments using input patterns of 3,171 character categories, including Kanji, Kana, and alphanumerics, written by 36 people in the cursive style against square-style reference patterns show that the proposed method not only can absorb a fairly large amount of handwriting fluctuation within the same category, but the discrimination ability is greatly improved by the suppression of excessive normalization against similarly shaped but different categories. Furthermore, comparative results obtained by the conventional shape normalization method for preprocessing are presented     

无源毫米波海岸线图像的边缘提取方法研究

分析了无源毫米波辐射图像轮廓特征,讨论了基于二阶微分算子与形态学的图像边缘检测的方法,根据海岸线的无源毫米波辐射特性和成像特点对图像区域标识,然后利用自适应阈值操作实现多目标区域的细分,最后采用形态学边缘检测海岸线特征方法。实验结果表明：对比传统的微分算子处理结果,形态学算法具有较高的检测精度,适用于提取无源毫米波辐射图像边缘。     

激光激发下拉曼光谱相对辐射定标模型研究

为了提高变压器油中气体检测性能,提出基于激光激发下拉曼光谱的相对辐射定标模型,采用激光成像技术进行拉曼光谱图像成像处理,提取拉曼光谱图像的边缘轮廓特征量,采用多尺度小波降噪方法进行激光激发下拉曼光谱图像降噪滤波,在邻域内采用颜色梯度分解方法进行拉曼光谱图像区域融合性增强处理,根据激光激发下拉曼光谱图像RGB值与邻域均值实现拉曼光谱相对辐射标定和检测,提高拉曼光谱图像的相对辐射定标能力。仿真结果表明,采用该方法进行激光激发下拉曼光谱相对辐射定标的准确性较高,识别精度较高,图像信息增强性能较好。     

An algorithm for line clipping against a polygon based on shearing transformation

Line clipping against a polygon is widely used in computer graphics such as the hidden line problem. A newline‐clipping algorithm against a general polygon is presented in this paper. The basic idea of this algorithm is tochange the line to be clipped into a horizontal line by shearing transformation. Then each edge of the polygonalwindow is transformed by a shearing transformation with the same parameters as those used to the line. Eachedge of the polygon is processed against a horizontal line, which makes the clipping process simpler. The result inthis paper shows that less calculation is needed for the new algorithm with a higher speed compared to existingalgorithms.     

The impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes

The k-nearest-neighbour (kNN) algorithm is widely applied for the estimation of forest attributes using remote sensing data. It requires a large amount of reference data to achieve satisfactory results. Usually, the number of available reference plots for the kNN-prediction is limited by the size of the area covered by a terrestrial reference inventory and remotely sensed imagery collected from one overflight. The applicability of kNN could be enhanced if adjacent images of different acquisition dates could be used in the same estimation procedure. Relative radiometric calibration is a prerequisite for this. This study focuses on two empirical calibration methods. They are tested on adjacent LANDSAT TM scenes in Austria. The first, quite conventional one is based on radiometric control points in the overlap area of two images and on the determination of transformation parameters by linear regression. The other, recently developed method exploits the kNN-cross-validation procedure. Performance and applicability of both methods as well as the impact of phenology are discussed.     

Feature based robust watermarking using image normalization

This paper presents a robust watermarking scheme based on feature point detection and image normalization. Firstly some stable feature points are detected from the original image using the proposed multiresolution feature point detection filter. Then, image normalization is applied to the disks centered at these feature points. The watermark is embedded in the subband coefficients of DFT domain of each disk separately. And the watermark detection uses the correlation between the watermark embedding coefficients and the original watermark, and does not need the original image. The proposed scheme combines the advantages of feature point detection and image normalization, which can achieve strong robustness to signal processing and geometrical distortions. The experimental results also demonstrate good performance of the proposed scheme.     

基于字符规范化的文档图像水印算法

运用字符规范化和小波变换的知识,提出一种将文档图像分割成字符图像,再对字符图像规范化,然后将随机序列嵌入到小波图像低频系数的水印算法.根据视觉系统纹理掩蔽特性,将不同强度的水印分量嵌入到了不同的小波系数中.由于文档图像分割和规范化本身具有抗几何攻击的特性,故该方法对缩放、小角度旋转有一定的鲁棒性,实验结果表明:该方法在文档图像上比其它方法更具备优越性.     

基于透射率归一化的单幅图像去雾算法

在有雾天气条件下拍摄的图像,由于光线在传播人过程中受到空气中悬浮颗粒的散射,导致图像内容模糊不清,颜色偏灰白色。为了恢复出清晰的图像,根据大气散射物理模型,提出一种单幅图像去雾算法。首先,该算法从大气散射模型出发,对大气光模型进行变形化简,得到新的去雾模型。然后对大气光值估计,并利用在局部统计上不相关性恢复透射率。最后,对透射率进行归一化处理,带入模型,得到恢复图像。实验结果表明,该算法能够很好地恢复图像的对比度和保持图像的真实颜色。