Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high range precision and low laser power. A multispectral single-photon Lidar system provides additional spectral diversity, allowing the discrimination of different materials. However, the main drawback of such systems can be the long acquisition time needed to collect enough photons in each spectral band. In this work, we tackle this problem in two ways: first, we propose a Bayesian 3D reconstruction algorithm that is able to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions. In contrast to previous algorithms, the novel method processes jointly all the spectral bands, obtaining better reconstructions using less photon detections. The proposed model promotes spatial correlation between neighbouring points within a given surface using spatial point processes. Secondly, we account for different spatial and spectral subsampling schemes, which reduce the total number of measurements, without significant degradation of the reconstruction performance. In this way, the total acquisition time, memory requirements and computational time can be significantly reduced. The experiments performed using both synthetic and real single-photon Lidar data demonstrate the advantages of tailored sampling schemes over random alternatives. Furthermore, the proposed algorithm yields better estimates than other existing methods for multi-surface reconstruction using multispectral Lidar data.

This paper proposes a new motion estimation method based on convolutional sparse coding to adaptively design the colored-coded apertures in static and dynamic spectral videos. The motion in a spectral video is estimated from a low-resolution reconstruction of the datacube by training a convolutional dictionary per spectral band and solving a minimization problem. Simulations show improvements in terms of peak signal-to-noise ratio (of up to 2 dB) of the reconstructed videos by using the proposed approach, compared with state-of-art non-adaptive coded apertures.

This paper investigates a new regularization of the group-sparsity estimation problem based on a quadratic envelope operator. The resulting estimator is shown to have a reduced bias when compared to the classical LASSO estimator and is characterized by a simple hyperparameter selection. Numerical results show that the quadratic envelope regularization yields estimates equal to an oracle solution with high probability. The robustness of the proposed hyperparameter selection rule is also analyzed.

One of the main objectives of hyperspectral image processing is to detect a given target among an unknown background. The standard data to conduct such a detection is a reflectance map, where the spectral signatures of each pixel’s components, known as endmembers, are associated with their abundances in the pixel. Due to the low spatial resolution of most hyperspectral sensors, such a target occupies a fraction of the pixel. A widely used model in case of subpixel targets is the replacement model. Among the vast number of possible detectors, algorithms matched to the replacement model are quite rare. One of the few examples is the Finite Target Matched Filter, which is an adjustment of the well-known Matched Filter. In this paper, we derive the exact Generalized Likelihood Ratio Test for this model. This new detector can be used both with a local covariance estimation window or a global one. It is shown to outperform the standard target detectors on real data, especially for small covariance estimation windows.

Detecting a target of known spectral signature from an unknown background is one of main goal of hyperspectral imaging. As the majority of hyperspectral imaging systems have a poor spatial resolution, subpixel targets are usual. In this case, the so-called replacement model is commonly advocated. This model, valid for reflectance images, specifies that if a target is present, the amount of background should reduce in the same proportion. Nevertheless, the majority of the standard detectors, such as the Match Filter or the Kelly detector, have been developed for different contexts, and do not exploit this constraint. One of the rare example that is suitable for the replacement model is the Finite Target Match Filter, which is known to improve the target selectivity detection. In this paper, we develop the exact Generalized Likelihood Ratio Test for the model at hand. We show that this new detector outperforms the standard ones, on a real data experiment.

Spacecraft health monitoring from housekeeping telemetry data represents one of the main issues in space operations. Motivated by the success of machine learning or data driven-based methods in many signal and image processing applications, some of these methods have been applied to anomaly detection in housekeeping telemetry via a semi-supervised learning. This paper studies a new multivariate anomaly detection algorithm based on a sparse decomposition on a dictionary of nominal patterns. One originality of the proposed method is a multivariate framework allowing us to take into account possible relationships between different telemetry parameters, in particular through a joint processing of time-series described by mixed continuous and discrete parameters. The proposed method is tested with real satellite telemetry and evaluated on a representative anomaly dataset composed of actual anomalies that occurred on several operated satellites. The first results confirm the interest of the proposed method and demonstrate its competitiveness with respect to the state-of-the-art.

Single-photon lidar devices can acquire 3D data at very long range with high precision. Moreover, recent advances in lidar arrays have enabled acquisitions at very high frame rates. However, these devices place a severe bottleneck on the reconstruction algorithms, which have to handle very large volumes of noisy data. Recently, real-time 3D reconstruction of distributed surfaces has been demonstrated obtaining information at one wavelength. Here, we propose a new algorithm that achieves color 3D reconstruction without increasing the execution time nor the acquisition process of the realtime single-wavelength reconstruction system. The algorithm uses a coded aperture that compresses the data by considering a subset of the wavelengths per pixel. The reconstruction algorithm is based on a plug-and-play denoising framework, which benefits from off-the-shelf point cloud and image de-noisers. Experiments using real lidar data show the competitivity of the proposed method.

Named Data Networking (NDN) proposes to change the core of the Internet. Based on mechanisms successfully used in P2P or CDN, it focuses on content and thus the Quality of Experience of users. Such an ambitious plan raises great challenges : caching, multipath, multi-producers, multi-consumers and security. This paper focuses on one of them: the control of congestion. Several studies have proposed congestion control solutions that fall into three kinds: the end-to-end solution, the hop-by-hop type and the hybrid one. However, the community lacks proper evaluations of such specific algorithms. In this work, we have implemented representative solutions on ndnSIM. In a first step, we have tested them on a small scale topology to ease their performance analysis and highlight their strengths and weaknesses. We complete this study with simulations on larger networks in order to confirm our conclusions. Furthermore, all results are reproducible. Eventually, the paper drives a discussion on how application needs could be considered in the design of a NDN congestion control.

In space operations, spacecraft health monitoring and failure prevention are major issues. This important task can be handled by monitoring housekeeping telemetry time series using anomaly detection (AD) techniques. The success of machine learning methods makes them attractive for AD in telemetry via a semi-supervised learning. Semi-supervised learning consists of learning a reference model from past telemetry acquired without anomalies in the so-called learning step. In a second step referred to as test step, most recent telemetry time-series are compared to this reference model in order to detect potential anomalies. This paper presents an extension of an existing AD method based on a sparse decomposition of test signals on a dictionary of normal patterns. The proposed method has the advantage of accounting for possible relationships between different telemetry parameters and can integrate external information via appropriate weights that allow detection performance to be improved. After recalling the main steps of an existing AD method based on a sparse decomposition [1] for multivariate telemetry data, we investigate a weighted version of this method referred to as W-ADDICT that allows external information to be included in the detection step. Some representative results obtained using an anomaly dataset composed of actual anomalies that occurred on several satellites show the interest of the proposed weighting strategy using external information obtained from the correlation coecient between the tested data and its decomposition on the dictionary.