Multimodal Remote Sensing Image Registration with Accuracy Estimation at Local and Global Scales

This paper focuses on potential accuracy of remote sensing images registration. We investigate how this accuracy can be estimated without ground truth available and used to improve registration quality of mono- and multi-modal pair of images. At the local scale of image fragments, the Cramer-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 detecting and discarding outliers and refining the estimation of geometrical transformation model parameters. Based on these ideas, a new area-based registration method called RAE (Registration with Accuracy Estimation) is proposed. In addition to its ability to automatically register very complex multimodal image pairs with high accuracy, the RAE method provides registration accuracy at the global scale as covariance matrix of estimation error of geometrical transformation model parameters or as point-wise 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. 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.Comment: 48 pages, 8 figures, 5 tables, 51 references Revised arguments in sections 2 and 3. Additional test cases added in Section 4; comparison with the state-of-the-art improved. References added. Conclusions unchanged. Proofrea

Molecular nitrogen in N doped TiO2 nanoribbons

The nitrogen doping of TiO2 nanoribbons during the thermal transformation of hydrogen titanate nanoribbons HTiNRs between 400 and 650 C in a dynamic ammonia atmosphere was investigated using X ray photoelectron spectroscopy XPS , transmission X ray microscopy combined with near edge X ray absorption fine structure spectroscopy NEXAFS TXM , X ray diffraction XRD and electron paramagnetic resonance measurements EPR . Comprehensive structural characterizations have revealed that for a calcination temperature of 400 C, the HTiNRs transform into pure monoclinic TiO2 b phase TiO2 B whereas at higher calcination temperatures 580 and 650 C a mixture of TiO2 B and anatase is obtained. XPS and EPR results clearly reveal the nitrogen doping of TiO2 nanoribbons and that, depending on the calcination temperature, nitrogen atoms occupy interstitial and substitutional sites. Moreover, in samples calcined at 580 and 650 C the presence of N2 like species in the HTiNRs was detected by NEXAFS TXM. These species are trapped in the HTiNRs structure. EPR measurements upon light illumination have disclosed the generation of photoexcited states which implies that nitrogen has an important effect on the electronic structure of N doped TiO

Estimation aveugle de la variance d'un bruit dépendant du signal

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PRINCIPAL COMPONENTS VERSUS AUTOENCODERS FOR DIMENSIONALITY REDUCTION: A CASE OF SUPER-RESOLVED OUTPUTS FROM PRISMA HYPERSPECTRAL MISSION DATA

International audienceThis study attempts to solve these issues associated with hyperspectral (HS) data, i.e., coarse spatial resolution and high volume, by understanding the effect of deep learning and traditional dimensionality reduction on super-resolved products generated from the recently launched PRecursore IperSpettrale della Missione Applicativa (PRISMA) HS mission. Four single-frame super-resolution (SR) algorithms have been used to super-resolve a 30 m PRISMA scene of Ahmedabad, India and generate 15 m spatial resolution images with both spatial and spectral fidelity. Iterative back projection (IBP) and sparse representation (SIS) are the best and worst-performing SR algorithms following a comparative assessment and validation protocol. Next, denoising autoencoders and PCT computed using singular and eigenvalue decompositions have been executed on the original PRISMA, IBP and SIS-based super-resolved datasets. The resulting low-dimensional representations have been assessed to preserve the original dataset's topology using label-independent Lee and Verleysen's co-ranking matrix and loss of quality measure. Findings suggest that autoencoders are computationally expensive and require a higher neighbourhood size than PCT and its variants to produce a high-quality encoding. These insights remain significant for urban information extraction as there are few direct comparative assessments between machine learning-based linear and non-linear data compression methods in earlier studies

Comparison of learning-based and maximum-likelihood estimators of image noise variance for real-life and synthetic anisotropic textures

International audienceProcessing of remote sensing data is often based on an assumption that noise parameters in component images are a priori known. If this assumption is not valid, it is desired to perform estimation of noise parameters directly from noisy image patches. In this paper, two estimators - model- and learning-based ones possessing the ability to evaluate noise standard deviation (SD) or variance and to predict estimation accuracy for each image patch are considered. The former approach is the representative of maximum likelihood estimator (MLE) of parameters for anisotropic fractional Brownian motion (afBm) field whilst the learning-based one is the representative of convolutional neural networks (CNN) that employs training on real-life images. Our goal is to compare the performance for two cases: for pure afBm data and for real-life images. It is shown that the learning-based approach occurs to be less effective for pure afBm data since it produces a certain bias whilst the model-based approach runs into problems for complex image patches in reallife images. Based on this analysis, we propose to use synthetic afBm data as additional source of training data for learning-based methods of noise parameters estimation. By mixing real and synthetic data for training of the NoiseNet CNN, we were able to improve its performance in both domains. For afBm data, NoiseNet bias was significantly reduced and ability to predict noise SD estimates confidence improved. On NED2012 database of real images, the modified NoiseNet reduces signal-independent noise SD component estimation error by about 40% as compared to the original CNN version. © SPIE. Downloading of the abstract is permitted for personal use only

Analysis of signal-dependent sensor noise on JPEG 2000-compressed sentinel-2 multi-spectral images

International audienceThe processing chain of Sentinel-2 Multi Spectral Instrument (MSI) data involves filtering and compression stages that modify MSI sensor noise. As a result, noise in Sentinel-2 Level-1C data distributed to users becomes processed. We demonstrate that processed noise variance model is bivariate: noise variance depends on image intensity (caused by signal-dependency of photon counting detectors) and signal-To-noise ratio (SNR; caused by filtering/compression). To provide information on processed noise parameters, which is missing in Sentinel-2 metadata, we propose to use blind noise parameter estimation approach. Existing methods are restricted to univariate noise model. Therefore, we propose extension of existing vcNI+fBm blind noise parameter estimation method to multivariate noise model, mvcNI+fBm, and apply it to each band of Sentinel-2A data. Obtained results clearly demonstrate that noise variance is affected by filtering/compression for SNR less than about 15. Processed noise variance is reduced by a factor of 2 - 5 in homogeneous areas as compared to noise variance for high SNR values. Estimate of noise variance model parameters are provided for each Sentinel-2A band. Sentinel-2A MSI Level-1C noise models obtained in this paper could be useful for end users and researchers working in a variety of remote sensing applications. © 2017 SPIE

Performance analysis of similarity measures between multichannel optical and multipolarization radar images

International audienceThis paper investigates the problem of measuring similarity between multimodal Remote Sensing (RS) images using both area-based and feature-based structural similarity measures (SMs). For many RS platforms, optical image is multichannel and radar image is multipolarization. Thus, vector-to-vector SMs could be applied to optical-to-radar image pairs in contrast to scalar-to-scalar SMs considered in the literature. Using two real Landsat8 - SIR-C image pairs, we demonstrate that vector variants of state-of-the-art SMs outperform their scalar counterparts. This is especially evident for Normalized Correlation Coefficient (NCC), which in vector case performs as good as or better than advanced structural SMs. © 2017 IEEE

Potential MSE of color image local filtering in component-wise and vector cases

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