Monitoring and detection of ships and oil spills using synthetic aperture radar (SAR) have received a considerable attention over the past few years, notably due to the wide area coverage and day and ...night all-weather capabilities of SAR systems. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators. The performance of the DoP is studied for joint ship and oil-spill detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on RADARSAT-2 C-band polarimetric data sets, over San Francisco Bay, and L -band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico.
Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform ...traditional transform coding schemes in terms of rate-distortion trade-off; however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes.
Abstract Objective The aim of this study was to evaluate the information pregnant women received regarding possible exposures to five recognized reprotoxic agents during their pregnancy. Study design ...A cohort study was conducted using two postnatal units in France. Women hospitalized in postnatal units were requested to complete a self-administered two part questionnaire. The first part gathered information about the patient’s socio-professional level and the type of pregnancy follow-up. The second part examined the information the patient received regarding daily products containing the following known reprotoxic agents: bisphenol A, toluene, n-hexane, cis-chloroallyl-triaza-azonia-adamantane-chloride and O-phenyl-phenol. The women cited the sources of information. We combined the employment status and educational level to separate the women into two groups. The groups were then compared using the Chi Square test or Fisher’s exact test. Result(s) There were 390 women in this study. Our results showed the women received information regarding the following: 21.6% (n = 84) regarding tin cans, 21.9% (n = 85) concerning plastic meal boxes when heated in microwave ovens, 8.8% (n = 32) about water in gas-bottles, 27.4% (n = 106) about non-organic foods, 39.3% (n = 152) about hair dyes, 17% (n = 66) about nail polishes, 23.4% (n = 103) about insect repellents, 34.4% (n = 133) about “do-it-yourself” products, 2.1% (n = 8) about gardening products, 26.7% (n = 103) about electric plug-in repellents, 21.1% (n = 81) about housekeeping products, and 6.8% (n = 26) about register receipts. Women with a higher level of education and a qualified occupation were better informed about these daily products. These women were more likely to learn the information on their own (internet, media). Conclusion(s) Our study showed French women did not receive sufficient information regarding potential exposures to reprotoxic agents during pregnancy.
Change detection (CD) is one of the most challenging issues when analyzing remotely sensed images. Comparing several multidate images acquired through the same kind of sensor is the most common ...scenario. Conversely, designing robust, flexible, and scalable algorithms for CD becomes even more challenging when the images have been acquired by two different kinds of sensors. This situation arises in the case of emergency under critical constraints. This paper presents, to the best of our knowledge, the first strategy to deal with optical images characterized by dissimilar spatial and spectral resolutions. Typical considered scenarios include CD between panchromatic, multispectral, and hyperspectral images. The proposed strategy consists of a three-step procedure: 1) inferring a high spatial and spectral resolution image by fusion of the two observed images characterized one by a low spatial resolution and the other by a low spectral resolution; 2) predicting two images with, respectively, the same spatial and spectral resolutions as the observed images by the degradation of the fused one; and 3) implementing a decision rule to each pair of observed and predicted images characterized by the same spatial and spectral resolutions to identify changes. To quantitatively assess the performance of the method, an experimental protocol is specifically designed, relying on synthetic yet physically plausible change rules applied to real images. The accuracy of the proposed framework is finally illustrated on real images.
Unsupervised change detection techniques are generally constrained to two multi-band optical images acquired at different times through sensors sharing the same spatial and spectral resolution. In ...the case of the optical modality, largely studied in the remote sensing community, a straight comparison of homologous pixels such as pixel-wise differencing is suitable. However, in some specific cases such as emergency situations, punctual missions, defense and security, the only available images may be those acquired through different kinds of sensors with different resolutions. Recently some change detection techniques, dealing with images with different spatial and spectral resolutions, have been proposed. Nevertheless, they are focused on a specific scenario where one image has a high spatial and low spectral resolution while the other has a low spatial and high spectral resolution. This paper addresses the problem of detecting changes between any two multi-band optical images disregarding their spatial and spectral resolution disparities. To overcome resolution disparity, state-of-the art methods apply conventional change detection methods after preprocessing steps applied independently on the two images, e.g. resampling operations intended to reach the same spatial and spectral resolutions. Nevertheless, these preprocessing steps may waste relevant information since they do not take into account the strong interplay existing between the two images. Conversely, in this paper, we propose a method that more effectively uses the available information by modeling the two observed images as spatially and spectrally degraded versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions. Covering the same scene, the latent images are expected to be globally similar except for possible changes in spatially sparse locations. Thus, the change detection task is envisioned through a robust fusion task which enforces the differences between the estimated latent images to be spatially sparse. We show that this robust fusion can be formulated as an inverse problem which is iteratively solved using an alternating minimization strategy. The proposed framework is implemented for an exhaustive list of applicative scenarios and applied to real multi-band optical images. A comparison with state-of-the-art change detection methods evidences the accuracy and the versatility of the proposed robust fusion-based strategy.
•Detecting changes between two optical images is formulated as a robust fusion.•The proposed method handles all combination of spatial and spectral image resolutions.•Robust fusion is performed using an alternating minimization algorithm.•All steps of the algorithm boil down to conducting a standard image processing task.•The performance is assessed on a comprehensive set of experiments.
This paper describes an original statistical approach for the lifespan modeling of electric machine insulation materials. The presented models aim to study the effect of three main stress factors ...(voltage, frequency, and temperature) and their interactions on the insulation lifespan. The proposed methodology is applied to two different insulation materials tested in partial discharge regime. Accelerated ageing tests are organized according to experimental optimization methods in order to minimize the experimental cost while ensuring the best model accuracy. In addition to classical parametric models, the life-stress relationship is expressed through original nonparametric and hybrid models that have never been investigated in insulation aging studies before. These two models present the original contribution of this paper. For each material, models are computed from organized sets of experiments and applied on a randomly configured test set for validity checking. The different models are evaluated and compared in order to define their optimal use.
Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images ...available may be those acquired through sensors of different modalities. This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by sensors of different modalities with possibly different resolutions. These sensor dissimilarities introduce additional issues in the context of operational change detection that are not addressed by most of the classical methods. This paper introduces a novel framework to effectively exploit the available information by modeling the two observed images as a sparse linear combination of atoms belonging to a pair of coupled overcomplete dictionaries learnt from each observed image. As they cover the same geographical location, codes are expected to be globally similar, except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a dual code estimation which enforces spatial sparsity in the difference between the estimated codes associated with each image. This problem is formulated as an inverse problem which is iteratively solved using an efficient proximal alternating minimization algorithm accounting for nonsmooth and nonconvex functions. The proposed method is applied to real images with simulated yet realistic and real changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy.
•Multimodal change detection is formulated as a coupled dictionary learning problem.•The method is able to handle multimodal images with possibly different resolution.•Coupled dictionary learning and codes estimated using alternating optimization.•Algorithmic solution for nonconvex problem with convergence to critical point.•The performance is assessed on a comprehensive set of experiments.
Modeling the lifespan of an organic light-emitting diode (OLED) is a complex task as it depends on different potentially interacting factors. As the literature on this subject is still scant, new ...parametric models for calculating the lifespan of the OLED are proposed in this article. The design of experiment (DoE) methodology is used for cost and accuracy reasons. Different lifespan models based on thermal and electrical experimental aging tests are proposed. As stress factors, current density, temperature, and their interactions, which are rarely taken into account in aging studies, are simultaneously involved. The analysis of the model parameters highlights the prevalence of temperature compared to current density on the luminance performance of OLEDs. Nonlinear models appear as the most accurate.
PDF
