Data stream partitioning is an important technique in data mining to analyze data streams in real-time. In this context, lots of data stream partitioning methods have been proposed. In the state of the art, most existing methods need to specify the number of classes before partitioning and/or introduce user-defined parameters for which the parameter values may differ for different data sets. In practice, it is difficult to determine the number of classes and the parameter values. Therefore, we propose in this paper an unsupervised and non-parametric method based on the Optimized Fuzzy C-Means algorithm. It has 2 steps. First is to partition a series of data chunks and then partition the intermediate classes formed before. The performance of the proposed algorithm is evaluated and compared with the recent state-of-the-art methods on hyperspectral image data sets.

Multichannel systems of remote sensing provide a huge amount of data useful for different applications. However, such images occupy a large space that poses problems of processing, storage, transmission, and management. Lossy compression is widely used to decrease the size of data. In lossy compression, one has to provide a reasonable trade-off between compression ratio (CR) and introduced losses or quality of compressed data. Quality can be characterized in various ways including traditional criteria as peak signal-to-noise ratio (PSNR) or some others as well as criteria that describe efficiency of solving the final tasks of remote sensing as, e.g., probability of correct classification. In this paper, we concentrate on classification of three-channel images that can be either color images or three components of multi- or hyperspectral data acquired, e.g., by Sentinel-2 sensor. In lossy compression of color images, downscaling of color components is often applied to increase CR without essential loss of quality. The goal of this paper is to study the influence of such downscaling on classification accuracy for three-channel remote sensing data. The compression method based on atomic functions is considered since this method allows easy control of compressed image quality and its providing. The neural networks trained for distorted-free images are applied for image classification. Analysis is carried out for four images of different complexity. Based on it, practical recommendations are given.

The exhaustive search of multiple matches in an overlapping area of two multimodal remote sensing images and the accurate localization of found matches are inherent steps to an efficient registration of these two images. A supervised approach based on convolutional neural networks can address this challenge by producing a similarity map, identifying potential matches within a preset search area and estimating a covariation matrix of their location errors. The training is based on a specially designed loss function to enforce the translational and rotational invariance of the similarity map. Using synthetic samples from anisotropic fractal Brownian motion (afBm) models of different orientation, we made the experimental finding that this loss function is biased with respect to orientation. This bias problem is then addressed by beneficially adding pure afBm data to the learning process.

Data stream partitioning has attracted more and more attention in processing large-scale data. The use of parametric methods to perform this task requires for each application an empirical tuning of the different parameters. In practice, this step is difficult to perform and often does not lead to an optimized partitioning result. To avoid this difficulty and provide an objective and optimized partitioning, we propose in this paper an unsupervised and non-parametric algorithm of data stream which employs the Optimized Fuzzy C-Means algorithm. The partitioning is first performed on a series of data chunks and the final partition is obtained by a fusion process of the intermediate classes formed before. The performance of the proposed algorithm is evaluated and compared with a recent state-of-the-art algorithm on hyperspectral image databases.

Remote sensing (RS) digital images have a great variety of applications in solving real-world problems. Modern sensors provide this type of data of a very high resolution, which, in combination with a great number of acquired images, makes a problem of compressing RS-images of particular importance. In this letter, discrete atomic compression (DAC) and a problem of its spatial complexity reduction are considered. This approach provides data compression and protection features in combination with such image representation that is ready for applying different artificial intelligence methods. For this reason, its application to image processing is relevant. Several modifications that provide reducing the spatial complexity of DAC are proposed, and their efficiency is analyzed. In particular, it is shown that, using a block splitting procedure, it is possible to get a significant decrease in additional memory expenses without DAC’s efficiency degradation in terms of lossy image compression.

This paper considers the problem of learning efficient similarity measure (SM) for multimodal remote sensing (RS) images. It is desirable to have a single SM that is efficient for different combinations of modes. We first consider the influence of training dataset balancing on SM efficiency. We demonstrate that it is possible to improve overall SM performance. However, this improvement is observed only for some combinations of modes. To cope with this problem, we propose to include information about the modes compared as additional input to the proposed Convolutional Neural Network (CNN). With this additional information, SM performance for all combinations of modes was improved. We confirm SM efficiency improvement for the real data from Sentinel 2, Landsat 8, Hyperion, SIR-C, and Sentinel 1 platforms, ASTER Global DEM 2, and ALOS World 30m global DEMs and for combinations of modes including optical-to-optical, optical-to-radar, optical-to-DEM and radar-to-DEM and compare the proposed CNN performance with existing SMs.

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.

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.

This paper deals with the problem of measuring similarity between visual and radar remote sensing images. It is proposed to combine the benefits of a finite set of representative Similarity Measures (SM) to obtain a combined SM with improved performance in terms of usual assessment criteria (ROC, AUC and LR+). This combined SM relies on a binary linear support vector machines (SVM) classifier trained using real visual-to-radar image pairs RS images. The best combination of SMs among those considered in the finite set is found to be SIFT-OCT, MIND and 10gLR SMs. It reaches a value of AUC criterion about 0.05 higher than that obtained by the best individual SM. This obtained gain is mainly attributed to the complementary properties of structural (SIFT-OCT, MIND) and area-based (logLR) SMs.

Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. In this study, we address the semiblind restoration of the degraded images component-wise, according to a sequential scheme. We propose a new component-wise semi-blind method for estimating effectively and accurately both the blur and the corresponding latent image. To prove applicability and higher efficiency of the proposed method, we compare it against the method it originates from. Our attention is mainly paid to the objective analysis (via l1-norm) of the estimation error accuracy. The tests are performed on a synthetic hyperspectral image. This image has been successively degraded with eight real blurs taken from the literature, each of a different support size. Conclusions, practical recommendations and perspectives are drawn from the results experimentally obtained.

Images of different origin contain textures, and textural features in such regions are frequently employed in pattern recognition, image classification, information extraction, etc. Noise often present in analyzed images might prevent a proper solution of basic tasks in the aforementioned applications and is worth suppressing. This is not an easy task since even the most advanced denoising methods destroy texture in a more or less degree while removing noise. Thus, it is desirable to predict the filtering behavior before any denoising is applied. This paper studies the efficiency of texture image denoising for different noise intensities and several filter types under different visual quality criteria (quality metrics). It is demonstrated that the most efficient existing filters provide very similar results. From the obtained results, it is possible to generalize and employ the prediction strategy earlier proposed for denoising techniques based on the discrete cosine transform. Accuracy of such a prediction is studied and the ways to improve it are considered. Some practical recommendations concerning a decision to undertake whether it is worth applying a filter are given.

This paper deals with prediction of compression ratio (CR) in lossy compression of noisy remote sensing images using techniques based on discrete cosine transform (DCT). Properties of noise assumed additive (in original data or after proper variance stabilizing transform) are taken into account by setting quantization step (QS) proportional to noise standard deviation. It is shown that simple statistics of DCT coefficients in 8×8 blocks can be used for rather accurate prediction of CR. Functions employed in prediction are obtained in advance using curve regression into scatter-plots. The factors that have impact on prediction accuracy are studied. It is demonstrated that percentage of DCT coefficients that become zeroes after quantization can be a good input parameter for prediction. Applicability of the proposed CR prediction approach is confirmed by experiments with real-life multi- and hyperspectral data.

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 MLfBm(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 MLfBmestimator 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 MLfBmestimates 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.

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.

We focus on considering noise properties in hyperspectral images acquired by different sensors. An initial assumption is that signal-dependent and signal-independent components are present. Using modern methods of blind estimation of noise parameters from images at hand, contributions of signal-dependent and signal-independent noise components are evaluated and compared for real-life images. It is demonstrated that for some sub-bands, contribution of signal-independent components is prevailing whilst for other sub-band images, the situation is the opposite.

Our paper addresses a question of prediction compression ratio in lossy compression of remote sensing images by coders based on discrete cosine transform (DCT) taking into account noise present in these images. Quantization step is set fixed and proportional to noise standard deviation to provide compression in optimal operation point if it exists. Simple statistics of DCT coefficients is used for predicting compression ratio. Prediction dependences are obtained offline (in advance) and they occur to be quite simple and accurate. The influence of DCT statistics on prediction efficiency is analyzed. Accuracy of prediction is studied for real-life hyperspectral data compressed component-wise.

This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes.

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.

The problem of automatic detection of image areas that can be reliably selected for accurate estimation of additive noise standard deviation (STD), irrespectively to processed image properties, is considered in this paper. For getting accurate estimate of either texture or noise parameters involved, we distinguish two complementary image informative maps: (1) noise-informative (NI) map and (2) its complementary texture-informative (TI) map. The NI map is determined and iteratively upgraded based on the Fisher information on noise STD calculated in a single scanning window (SW). The TI map is simply evolved as the complementary part of N map currently updated. Final noise STD estimation is performed by efficient analysis of finite size 9×9 block DCT coefficients in NI SWs. Experiments on large image database have proved that the proposed approach outperforms state-of the-art estimators with respect to both noise STD estimates bias and variance.

The paper addresses blind evaluation of the parameters of mixed noise in images. The conventional approach is based on line fitting in the scatter-plot of local variance estimates using LMS algorithm. This does not utilize the fact that the points in the scatter pot typically appear in clusters that depend on the image. It is shown that the use of weighted LMS algorithm that takes into account the number of points in clusters provides considerable improvement in the accuracy of line fitting and, thus, better estimation of the parameters of mixed noise.

A novel approach to joint estimation of multiplicative noise variance and probability of impulsive noise occurrence in images is proposed. It uses a fractal Brownian motion model for description of real life images. It is demonstrated that this approach provides accurate estimation of mixed noise parameters even for images containing a large percentage of texture regions. The proposed method performance is compared to a modification of a recently designed method based on minimal inter-quantile distances.

Some peculiarities of modified vector sigma filter are studied. In particular, its edge preservation ability is considered in case of processing multichannel remote sensing (RS) images. Such a problem is of high importance for many scene recognition and segmentation tasks. It is demonstrated through comparative quantitative and visual processing data that the proposed filter simultaneously provides efficient noise suppression and excellent edge preservation. Edge detection results that prove this fact are also depicted

This paper deals with the problem of identifying the nature of the noise and estimating its statistical parameters from the observed image in order to be able to apply the most appropriate processing or analysis algorithm afterwards. We focus our attention on three main classes of degraded images, the first one being degraded by an additive noise, the second one by a multiplicative noise, and the latter by an impulse noise. To improve the identification rate, we propose an unsupervised variational classification through a multithresholding method. Each class is then characterized by statistical parameters obtained from homogeneous regions. For the accuracy of the estimation of the noise statistical parameters, we distinguish the corresponding local estimates statistical series according to the number of pixels taken into account to calculate them. The experimental study highlights the improvement so obtained and shows the efficiency and the robustness of the whole method

We present a new adaptive filtering method for either additive noise or multiplicative noise. The proposed method is stated with a differential equation of the temporal evolution of the problem of interest. It achieves an improvement in the efficiency of the well-known iterated Lee filter in the spatial domain. Mainly, it incorporates a local determination of the optimal regions which are subsequently used to estimate the different local statistics involved in the filtering method. This estimation is carried out differently according to the nature of the processed pixel. An adapted decisional criterion indicates if the pixel belongs either to a contour or to a homogeneous zone, following the idea used in classical anisotropic methods. Then, the efficiency of the proposed method, for which we can prove the existence and uniqueness of the solution, is assessed on several images degraded artificially. The results are compared to the main used filters in order to confirm the theoretical findings.

This paper presents a totally blind restoration system of blurred and noisy images. It includes two processing modules, one for identifying the blur PSF and another for blind image restoration. The blur PSF identification and estimation is achieved using the generalized cross-validation criterion and a smoothness constraint on the image. The deblurring step is based on a Tikhonov-Miller regularization technique. Simulation results show that this system yields rather good results on various kind of images with low signal-to-noise ratio. It is quite effective in restoring severely blurred and noise corrupted images, without prior knowledge of either the noise or image characteristics. It can also discriminate between blurred and unblurred images.

In pattern recognition problems, the effectiveness of the analysis depends heavily on the quality of the image to be processed. This image may be blurred and/or noisy and the goal of digital image restoration is to find an estimate of the original image. A fundamental issue in this process is the blur estimation. When the blur is not readily available, it has to be estimated from the observed image. Two main approaches can be found in the literature. The first one identify the blur parameters before any restoration whereas the second one realizes these two steps jointly. We present a comparative study of several parametric blur estimation methods, based on a parametric ARMA modeling of the image, belonging to the first approach. Our purpose is to evaluate the accuracy of the various methods, on which the restoration procedure relies, and their robustness to modeling assumptions, noise, and size of support.

This paper deals with the problem of identifying the nature of noise and estimating its standard deviation from the observed image in order to be able to apply the most appropriate processing or analysis algorithm afterwards. In this study, we focus our attention on three classes of degraded noise images, the first one being degraded by an additive noise, the second one by a multiplicative noise and the latter by an impulsive noise. First, in order to identify the nature of the noise, we propose a new approach consisting of characterizing each class by a parameter obtained from histograms computed on several homogeneous regions of the observed image. The homogeneous regions are obtained by segmenting images. Then, the estimation of the standard deviation is achieved from the analysis of an histogram of local standard deviations computed on each of the homogeneous regions.

The paper presents the results of a comparative study of the respective efficiency of three parametric signal processing methods to detect abrupt spectral changes by means of the detection of abrupt model discontinuities, while they were applied to the very particular case of inspection of change in myoelectric activity of surface electromyograms (EMG). The studied surface electromyograms are those of biceps brachii during a perturbed flexion-extension forearm movement in the horizontal plane. After the description of the experimental device, the position problem is then formally considered, and the different methods used are briefly recalled. Finally, the results observed on a large set of trials are shown to highlight the behaviour of each selected method before concluding on the opportunity to use them to characterize some neuropathies.