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.

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.

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 studies the problem of full reference visual quality assessment of denoised images with a special emphasis on images with low contrast and noise-like texture. Denoising of such images together with noise removal often results in image details loss or smoothing. A new test image database, FLT, containing 75 noise-free `reference' images and 300 filtered (`distorted') images is developed. Each reference image, corrupted by an additive white Gaussian noise, is denoised by the BM3D filter with four different values of threshold parameter (four levels of noise suppression). After carrying out a perceptual quality assessment of distorted images, the mean opinion scores (MOS) are obtained and compared with the values of known full reference quality metrics. As a result, the Spearman Rank Order Correlation Coefficient (SROCC) between PSNR values and MOS has a value close to zero, and SROCC between values of known full-reference image visual quality metrics and MOS does not exceed 0.82 (which is reached by a new visual quality metric proposed in this paper). The FLT dataset is more complex than earlier datasets used for assessment of visual quality for image denoising. Thus, it can be effectively used to design new image visual quality metrics for image denoising.

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.

Data provided by synthetic aperture radar (SAR) of Sentinel satellite can be useful for many applications. However, as for any SAR image, speckle noise is present in acquired images. Speckle properties are important for different operations of SAR image processing as filtering, edge detection, segmentation, classification. Thus, we first carry out preliminary analysis of speckle statistics and show that speckle PDF is quite close to Gaussian whilst noise is of practically multiplicative nature. Second, spatial correlation properties of speckle are analyzed. The study is performed in local DCT domain. This is done since then the obtained 2D spectrum is employed in image despeckling based on DCT. Peculiarities of several possible approaches to despeckling are discussed. Several examples for one component and dual polarization data are presented.

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.

This paper deals with one way to predict efficiency of despeckling for images acquired by a single-or multi-look synthetic aperture radar (SAR). A particular filter based on discrete cosine transform (DCT) and adapted to speckle characteristics is considered. Two quantitative criteria characterizing despeckling efficiency are analyzed. It is shown that even one parameter that can be calculated for image blocks sequentially or in parallel allows carrying out a rather accurate prediction. Moreover, such a prediction is possible for both spatially uncorrelated and correlated speckle.

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.

The paper contains comparisons of lower bound (potential) and achieved efficiency for filtering grayscale and color images corrupted by AWGN. It is demonstrated that for complex structure images the corresponding limits are practically reached. Then, the main problems of the current stage of filter design are discussed taking into account noise models more adequate for practice, inherent properties of multicomponent (e.g. color, hyperspectral, etc.) images, and other qualitative criteria than conventional MSE or PSNR criteria.

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.

In many practical applications, noise is non-Gaussian. Heavy-tailed symmetric alpha-stable (SalphaS) distributions have been shown to describe well many natural phenomena and interference in radio engineering, acoustics, communications, etc. For processing signals corrupted by such noise, robust estimators and the corresponding techniques are widely applied. The methods based on a sample myriad (SM) are considered quasi-optimal for removal of noise with SalphaS distributions. SM estimation implies setting a tunable parameter k, desirably in an adaptive manner. However, only quite approximate recommendations concerning the selection of k for a limited size samples exist. In this paper, we study some important properties of the SM estimator with application to SalphaS distributed data. Furthermore, we propose a novel bootstrap based approach to adaptation of the tunable parameter k. By simulations, we consider its accuracy and demonstrate the effectiveness of this approach for a wide range of SalphaS distribution characteristics. Practical recommendations on the selection of parameters for the bootstrap based approach are provided and justified.

In estimation of the efficiency for lossy image compression methods, standard sets of test images are commonly used. This allows the comparison of new techniques to existing methods without having to actually implement the existing technique. However, this does not allow adequate evaluation of the performance of the methods for compressing natural images. In this paper, we analyze the efficiency of a set of lossy compression techniques (JPEG, JPEG2000, HD Photo and ADCTC) using a set of images obtained by three consumer quality digital cameras.

Two automatic approaches to lossy compression of hyperspectral AVIRIS images are proposed and considered. A first approach (strategy) is to filter images on-board and then to transfer compressed. A second strategy assumes that image filtering is performed on-land applied to decompressed data. In both cases, blind evaluation of noise variance is carried out. For both strategies, sub-band images can be compressed component-wise or adaptively grouped and compressed using a modified 3D DCT based coder. It is shown that the latter (3D) technique provides considerably better results. The first strategy produces wider facilities of hyperspectral image manipulation on-land whilst for the second strategy larger compression ratio can be automatically provided. This is demonstrated for a set of real life AVIRIS images.

In this contribution, a new image database for testing full-reference image quality assessment metrics is presented. It is based on 1700 test images (25 reference images, 17 types of distortions for each reference image, 4 levels for each type of distortion). Using this image database, 654 observers from three different countries (Finland, Italy, and Ukraine) have carried out about 400000 individual human quality judgments (more than 200 judgments for each distorted image). The obtained mean opinion scores for the considered images can be used for evaluating the performances of visual quality metrics as well as for comparison and for the design of new metrics. The database, with testing results, is freely available.

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.

Lossy compression of AVIRIS hyperspectral images is considered. An automatic approach to selection of compression parameters depending on noise characteristics in component images is proposed. Several ways of performing lossy compression are discussed and compared. It is shown that in order to minimize distortions and provide a sufficient compression ratio it is reasonable to group the channels according to the evaluated noise variances in subband images and depending upon the sensor that produces sets of subband images. It is shown that for real life images the attained compression ratios can be of the order 8. ..25.

This letter presents an advanced discrete cosine transform (DCT)-based image compression method that combines advantages of several approaches. First, an image is divided into blocks of different sizes by a rate-distortion-based modified horizontal-vertical partition scheme. Statistical redundancy of quantized DCT coefficients of each image block is reduced by a bit-plane dynamical arithmetical coding with a sophisticated context modeling. Finally, a post-filtering removes blocking artifacts in decompressed images. The proposed method provides significantly better compression than JPEG and other DCT-based techniques. Moreover, it outperforms JPEG2000 and other wavelet-based image coders

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

The application of adaptive techniques for obtaining bispectrum estimates in additive Gaussian noise and random shifts of received signals is considered. An approach using joint adaptive robust forming of bispectrum estimates and processing of complex-valued signal Fourier spectrum estimates by discrete cosine transform-based filtering with local variance estimation within each block is proposed. The advantages of the proposed approach in comparison to the conventional signal waveform recovery from bispectrum are illustrated by computer simulations

Modification of the robust discrete Fourier transform (DFT) is proposed in order to achieve a high breakdown point for signals corrupted by complex-valued impulse noise with independent real and imaginary parts. Obtained results are compared with existing robust DFT forms. In addition, an adaptive procedure for selection of the modified robust DFT form is developed.

We propose an approach to fractal image compression that provides fast decoding of the compressed image in one iteration and allows knowing the accurate value of the error contributed by each range block to the collage error at each step of partition scheme optimization. A modification of this method assuming equal sizes of domain and range blocks is considered. The results of the proposed approach application to test images are analyzed. The further research directions are discussed.

Summary form only given. An approach based on applying Delaunay triangulation to compression of mean values of image blocks that have non-identical shape and size is proposed. It can be useful for image compression methods that require the use of image partition schemes with non-equal block size like fractal and DCT-based image coding. Several methods of block mean value coding are considered. In particular, the drawbacks of using quantization with further redundancy elimination by entropy coders are discussed. Another considered method is the forming of the block mean value image and its further compression by lossy coders. Finally, the motivations in favor of Delaunay triangulation application to block mean value image coding are presented.

A problem of speeding-up the fractal compression of still images is discussed. The techniques based on analysis of local variance for range blocks and domain blocks are proposed and considered. An algorithm for fast search of domain blocks corresponding to the range block in the best manner is described. It is shown that a reduction of search CPU time by several times can be provided and the obtained benefit depends upon the complexity of image to be compressed and the range block size. The proposed procedure for speeding up the fractal compression does not result in additional losses in recovered image quality.

Step-like weighting windows are introduced for various digital signal processing and antenna array applications. The usefulness of these windows lies in the fact that the weight values of the in-normalized windows are piecewise constants and take on only integer values, thereby making the data weighting very effective. In the case of antenna arrays, the weight values take on very few distinct values. The optimization and properties of these windows are considered and they are compared with their Dolph-Chebyshev and Kaiser-Bessel counterparts.