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Many similarity measures (SMs) were proposed to measure the similarity between multimodal remote sensing (RS) images. Each SM is efficient to a different degree in different registration cases (we consider visible-to-infrared, visible-to-radar, visible-to-digital elevation model (DEM), and radar-to-DEM ones), but no SM was shown to outperform all other SMs in all cases. In this article, we investigate the possibility of deriving a more powerful SM by combining two or more existing SMs. This combined SM relies on a binary linear support vector machine (SVM) classifier trained using real RS images. In the general registration case, we order SMs according to their impact on the combined SM performance. The three most important SMs include two structural SMs based on modality independent neighborhood descriptor (MIND) and scale-invariant feature transform-octave (SIFT-OCT) descriptors and one area-based logarithmic likelihood ratio (logLR) SM: the former ones are more robust to structural changes of image intensity between registered modes, the latter one is to image noise. Importantly, we demonstrate that a single combined SM can be applied in the general case as well as in each particular considered registration case. As compared to existing multimodal SMs, the proposed combined SM [based on five existing SMs, namely, MIND, logLR, SIFT-OCT, phase correlation (PC), histogram of orientated phase congruency (HOPC)] increases the area under the curve (AUC) by from 1% to 21%. From a practical point of view, we demonstrate that complex multimodal image pairs can be successfully registered with the proposed combined SM, while existing single SMs fail to detect enough correspondences for registration. Our results demonstrate that MIND, SIFT, and logLR SMs capture essential aspects of the similarity between RS modes, and their properties are complementary for designing a new more efficient multimodal SM. [ABSTRACT FROM AUTHOR]

In this article, we borrow from the blind noise parameter estimation (BNPE) methodology early developed in the image processing field an original and innovative no-reference approach to estimate digital elevation model (DEM) vertical error parameters without resorting to a reference DEM. The challenges associated with the proposed approach related to the physical nature of the error and its multifactor structure in DEM are discussed in detail. A suitable multivariate method is then developed for estimating the error in gridded DEM. It is built on a recently proposed vectorial BNPE method for estimating spatially correlated noise using noise informative areas and fractal Brownian motion. The new multivariate method is derived to estimate the effect of the stacking procedure and that of the epipolar line error on local (fine-scale) standard deviation and autocorrelation function width of photogrammetric DEM measurement error. Applying the new estimator to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM2 and Advanced Land Observing Satellite (ALOS) World 3D DEMs, good agreement of derived estimates with results available in the literature is evidenced. Adopted for TanDEM-X-DEM, estimates obtained agree well with the values provided in the height error map. In future works, the proposed no-reference method for analyzing DEM error can be extended to a larger number of predictors for accounting for other factors influencing remote sensing (RS) DEM accuracy. [ABSTRACT FROM AUTHOR]

This paper focuses on the potential accuracy of remote sensing (RS) image registration. We investigate how this accuracy can be estimated without ground truth available and used to improve registration quality of mono- and multimodal pair of images. At the local scale of image fragments, the Cramér–Rao lower bound (CRLB) on registration error is estimated for each local correspondence between coarsely registered pair of images. This CRLB is defined by local image texture and noise properties. Opposite to the standard approach, where registration accuracy is only evaluated at the output of the registration process, such valuable information is used by us as an additional input knowledge. It greatly helps in detecting and discarding outliers and refining the estimation of geometrical transformation model parameters. Based on these ideas, a new area-based registration method called registration with accuracy estimation (RAE) is proposed. In addition to its ability to automatically register very complex multimodal image pairs with high accuracy, the RAE method is able to provide registration accuracy at the global scale as a covariance matrix of estimation error of geometrical transformation model parameters or as pointwise registration standard deviation. This accuracy does not depend on any ground truth availability and characterizes each pair of registered images individually. Thus, the RAE method can identify image areas for which a predefined registration accuracy is guaranteed. This is essential for RS applications imposing strict constraints on registration accuracy such as change detection, image fusion, and disaster management. The RAE method is proved successful with reaching subpixel accuracy while registering eight complex mono-/multimodal and multitemporal image pairs including optical-to-optical, optical-to-radar, optical-to-digital elevation model (DEM) images, and DEM-to-radar cases. Other methods employed in comparisons fail to provide in a stable manner accurate results on the same test cases. [ABSTRACT FROM PUBLISHER]

This paper deals with area-based subpixel image registration under the rotation-isometric scaling-translation transformation hypothesis. Our approach is based on parametrical modeling of geometrically transformed textural image fragments and maximum-likelihood estimation of the transformation vector between them. Due to the parametrical approach based on the fractional Brownian motion modeling of the local fragments' texture, the proposed estimator \mboxML_\mathrmfBm (ML stands for “maximum likelihood” and fBm stands for “fractal Brownian motion”) has the ability to better adapt to real image texture content compared with other methods relying on universal similarity measures such as mutual information or normalized correlation. The main benefits are observed when assumptions underlying the fBm model are fully satisfied, e.g., for isotropic normally distributed textures with stationary increments. Experiments on both simulated and real images and for high and weak correlations between registered images show that the \mboxML_\mathrmfBm estimator offers significant improvement compared with other state-of-the-art methods. It reduces translation vector, rotation angle, and scaling factor estimation errors by a factor of about 1.75–2, and it decreases the probability of false match by up to five times. In addition, an accurate confidence interval for \mboxML_\mathrmfBm estimates can be obtained from the Cramér–Rao lower bound on rotation-scaling-translation parameter estimation error. This bound depends on texture roughness, noise level in reference and template images, correlation between these images, and geometrical transformation parameters. [ABSTRACT FROM AUTHOR]

A new performance bound is proposed for analyzing parametric image registration methods objectively. This original bound is derived from the Cramer-Rao lower bound on the estimation error of parameters involved in a geometric transformation assumed between reference and template images (pure translation in this work) and parameters describing the texture of these images. For describing local fragments of both the reference and the template images, the parametric fractional Brownian motion (fBm) model has been chosen. Experimental results, obtained first on pure fBm data with full matching of the data to the texture model assumption, give evidence that the proposed bound describes more adequately the performance of conventional estimators than two other bounds previously proposed in the literature. This holds with respect to the signal-to-noise ratio value of both images, the roughness of their texture, their correlation, and the actual value of translation parameters between their grids. Then, one real Hyperion hyperspectral data set is considered to test the proposed bound behavior on real data. The proposed bound is demonstrated to describe more adequately the estimation accuracy of the translation parameters between different bands of this data set. [ABSTRACT FROM PUBLISHER]

A new algorithm is described for estimating the noise model parameters in hyperspectral data when neither noise components variance nor noise spatial/spectral correlation priors are available. A maximum likelihood (ML) technique is introduced for checking the noise spatial correlation hypothesis and estimating the spatial correlation function width alongside with estimating signal-independent and signal-dependent noise components variance. The hyperspectral image is assumed to match a limited set of assumptions. A three-dimensional (3-D) fractional Brownian motion (fBm) model is introduced for describing locally the texture of the 3-D image noisy textural fragment. Nonstationarity of the useful image signal is taken into account by performing the estimation locally on a 3-D block-by-block basis. The accuracy of the proposed algorithm is first illustrated for synthetic images obtained from either pure fBm or almost noise-free AVIRIS hyperspectral images artificially degraded with spatially correlated noise. The results obtained for synthetic images demonstrate appropriate accuracy and robustness of the proposed method. Then results obtained for real life AVIRIS hyperspectral data sets confirm the noise spatial uncorrelation hypothesis for images acquired by the AVIRIS sensor. Conclusions and open problems are outlined. [ABSTRACT FROM AUTHOR]

A maximum-likelihood method for estimating hyperspectral sensors random noise components, both dependent and independent from the signal, is proposed. A hyperspectral image is locally jointly processed in the spatial and spectral dimensions within a multicomponent scanning window (MSW), as small as 7\times 7\times 7 spatial-spectral pixels. Each MSW is regarded as an additive mixture of spectrally correlated fractal Brownian motion (fBm)-samples and random noise. The main advantage of the proposed method is its ability to accurately estimate band noise variances locally by using spatial and spectral texture correlations from a single textural MSW. For each spectral band, both additive and signal-dependent band noise components are estimated by linear fit of local noise variances obtained from many MSWs distributed over the whole band intensity range. CRLB-based analysis of the estimator performance shows that a good compromise is to jointly process seven adjacent spectral bands. The proposed method performance is assessed first on synthetic fBm-data and on real images with synthesized noise. Finally, four different AVIRIS datasets from 1997 flying season are considered. Good coincidence between additive and signal-dependent AVIRIS random noise components estimates obtained by our method and the estimates retrieved from AVIRIS calibration data is demonstrated. These experiments suggest that it is worth taking into account noise signal-dependency hypothesis for processing AVIRIS data. [ABSTRACT FROM PUBLISHER]

Presents the use of fractal compression methods for image filtering. Inhomogeneous structure of images and their corruption by additive, multiplicative or pulse interferences; Image filtering and choice of image model; Compression and filtering of images with fractal compression method; Details of the application of the method for image filtering; Improvement in the quality of filtering due to joint use of near and far correlation links manifesting itself as a self-similarity of the image at various scaling.

The Bone Marrow Transplantation Program in Belarus was founded in 1992, and in 1993, a Bone Marrow Transplantation Centre was created in Minsk. From February 1994 to April 1996, 19 allogeneic bone marrow, 16 autologous bone marrow and 10 autologous peripheral blood stem cell transplantations were performed. Reasons for transplantation included chronic myeloid leukemia, multiple myeloma, severe aplastic anemia, acute myeloid leukemia, acute lymphoblastic leukemia, progressive myelofibrosis, Hodgkin's disease, non-Hodgkin's lymphoma, and neuroblastoma. Among the patients were two liquidators involved in the Chernobyl cleanup activity, both of whom underwent allogeneic bone marrow transplantation. A variety of ablative preparative regimens were used, and blood progenitor cells were mobilized by treatment with Cytoxan and granulocyte colony-stimulating factor. Therapy-related deaths resulted from graft-versus-host disease, septic shock, veno-occlusive disease bleeding and intestinal pulmonary fibrosis. Because the transplantation procedures were carried out on people who continued to be exposed to low-level irradiation, the post-transplantation period included a conservative strategy for prevention of graft-versus-host disease. There was nothing unusual about the post-transplantation period, although uncertainty about the continuing radiation dose should be taken into account when interpreting these data.