Non-local tensor completion for multitemporal remotely sensed images inpainting

Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. The paper aims at reconstructing the missing information by a non-local low-rank tensor completion method (NL-LRTC). First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then low-rankness of the identified 4-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared to other patch based methods such as the recent proposed PM-MTGSR method

A Comparison of Image Denoising Methods

The advancement of imaging devices and countless images generated everyday pose an increasingly high demand on image denoising, which still remains a challenging task in terms of both effectiveness and efficiency. To improve denoising quality, numerous denoising techniques and approaches have been proposed in the past decades, including different transforms, regularization terms, algebraic representations and especially advanced deep neural network (DNN) architectures. Despite their sophistication, many methods may fail to achieve desirable results for simultaneous noise removal and fine detail preservation. In this paper, to investigate the applicability of existing denoising techniques, we compare a variety of denoising methods on both synthetic and real-world datasets for different applications. We also introduce a new dataset for benchmarking, and the evaluations are performed from four different perspectives including quantitative metrics, visual effects, human ratings and computational cost. Our experiments demonstrate: (i) the effectiveness and efficiency of representative traditional denoisers for various denoising tasks, (ii) a simple matrix-based algorithm may be able to produce similar results compared with its tensor counterparts, and (iii) the notable achievements of DNN models, which exhibit impressive generalization ability and show state-of-the-art performance on various datasets. In spite of the progress in recent years, we discuss shortcomings and possible extensions of existing techniques. Datasets, code and results are made publicly available and will be continuously updated at https://github.com/ZhaomingKong/Denoising-Comparison.Comment: In this paper, we intend to collect and compare various denoising methods to investigate their effectiveness, efficiency, applicability and generalization ability with both synthetic and real-world experiment

Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation

Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the uncertainty quantification of the restored HSI has not been addressed to date. Accurate uncertainty quantification of the denoised HSI facilitates to applications such as multi-source or multi-scale data fusion, data assimilation, and product uncertainty quantification, since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise uncertainty quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of the HSI patch mixing problem caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the uncertainty of the observed HSI and provides the uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10-20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form uncertainty quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive. The code is available in the attachment and will be released after the acceptance of this paper.Comment: Accepted for publication by IEEE Transactions on Geoscience and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing (TGRS

Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition

Hyperspectral image (HSI) is usually corrupted by various types of noise, including Gaussian noise, impulse noise, stripes, deadlines, and so on. Recently, sparse and low-rank matrix decomposition (SLRMD) has demonstrated to be an effective tool in HSI denoising. However, the matrix-based SLRMD technique cannot fully take the advantage of spatial and spectral information in a 3-D HSI data. In this paper, a novel group sparse and low-rank tensor decomposition (GSLRTD) method is proposed to remove different kinds of noise in HSI, while still well preserving spectral and spatial characteristics. Since a clean 3-D HSI data can be regarded as a 3-D tensor, the proposed GSLRTD method formulates a HSI recovery problem into a sparse and low-rank tensor decomposition framework. Specifically, the HSI is first divided into a set of overlapping 3-D tensor cubes, which are then clustered into groups by K-means algorithm. Then, each group contains similar tensor cubes, which can be constructed as a new tensor by unfolding these similar tensors into a set of matrices and stacking them. Finally, the SLRTD model is introduced to generate noisefree estimation for each group tensor. By aggregating all reconstructed group tensors, we can reconstruct a denoised HSI. Experiments on both simulated and real HSI data sets demonstrate the effectiveness of the proposed method.This paper was supported in part by the National Natural Science Foundation of China under Grant 61301255, Grant 61771192, and Grant 61471167, in part by the National Natural Science Fund of China for Distinguished Young Scholars under Grant 61325007, in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, and in part by the Science and Technology Plan Project Fund of Hunan Province under Grant 2015WK3001 and Grant 2017RS3024.Peer Reviewe

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems