1,721,155 research outputs found
Atmospheric Column Water Vapor Retrieval From Hyperspectral VNIR Data Based on Low-Rank Subspace Projection
No full textThe knowledge of atmospheric column water vapor concentration is crucial for compensating water absorption effects in remote sensing data. Several algorithms for the estimation of such a parameter were proposed in the past. One of the most effective algorithm is the Atmospheric Precorrected Differential Absorption Technique (APDA). APDA relies on a simplified radiative transfer model (RTM) that does not account for the spatial variability of the adjacency effects In this paper, we study the impact of the simplified RTM assumption on the performance of the algorithm by exploiting a more realistic and well-established RTM. Starting from such a model, we derive a new water retrieval algorithm called Low Rank Subspace projection based Water Estimator (LRSWE). It exploits the high degree of spectral correlation experienced in the reflectances of most of the existing materials.
An extensive experimental analysis is carried out on simulated data in order to assess and compare the performance of the two algorithms. Simulation results allow the critical analysis of the two algorithms by highlighting their strengths and drawbacks
Mitigating the impact of signal-dependent noise on hyperspectral target detection
No full textA pre-processing procedure can diminish the data noise from new-generation hyperspectral sensors, thus minimizing negative impacts on target detection algorithms
Unsupervised Atmospheric Compensation of airborne hyperspectral images in the VNIR spectral range
No full textAtmospheric compensation is a fundamental and critical step for quantitative exploitation of hyperspectral data. It is the means by which the reflectance of an object/material is estimated from the measured at-sensor radiance. Such reflectance is the inherent signature that is used to identify various materials in a monitored scene. Atmospheric compensation is quite complex and is hampered by the large amount of uncontrollable variables that play a role: just think about the spatial variability of some atmospheric constituents such as water vapor and aerosols, or to the rapidly spatially varying effects of the radiation coming from adjacent areas. Though, in principle, some atmospheric parameters and radiometric quantities such as solar irradiance and sky irradiance can be measured during the flight, in practice such measures are rarely available in an operational framework or are taken at a single point of the surface ignoring their spatial variation. Thus, a prompt quantitative exploitation of hyperspectral data for operational purposes, such as material identification and object detection, requires unsupervised and accurate atmospheric compensation procedures that can learn from the image itself the parameters of the inversion model and follow their variability within the scene.
In this framework, we present a new unsupervised methodology for atmospheric compensation of airborne hyperspectral images in the Visible and Near Infra Red spectral range. The proposed methodology relies on a radiative transfer model accounting for the adjacency effect and allows the estimation of relevant atmospheric parameters. Specifically, it embeds two new algorithms for the estimation of 1) aerosol and atmospheric visibility and 2) the water vapor content of the atmosphere accounting for the spatial variability of such a parameter. The two algorithms significantly differ from those adopted by existing state of art approaches or in commercial packages like FLAASH and ATCOR.
In this paper, we present the detailed description of the new atmospheric compensation methodology, and we analyze the results provided by the algorithm over real data
Cyber-Physical Systems formalization in de- and remanufacturing and application to size reduction stage
Full text linkCircular economy aims to shift from traditional linear approach of "take, make, dispose" to a closed loop scenario with the final goals of waste reduction and improvement of environmental friendly processes, creating new adaptable and resilient systems. Recycling processes, fundamental in circular economy, are facing issues related to the variability both in input and in output, showing mechanical treatments as the most promising in terms of environmental impacts and costs. First step in every recycling line is shredding. The objective is to obtain liberated particles (composed only by target materials) at a desired dimensional distribution. Cyber-Physical Systems are the most promising technology in recycling due to their capability to enable working always with optimized parameters, with a continuous exchange of information and actuations between hardware and software parts. The scope of this work is to present an architecture to optimize comminution processes, divided in two different steps, also based on Cyber-Physical Systems. First one aims to achieve the target optimal distribution, while the second one to minimize operational costs
Subspace-Based Temperature and Emissivity Separation Algorithms in LWIR Hyperspectral Data
Full text linkIn this paper, we investigate the temperature and emissivity separation (TES) problem from hyperspectral data acquired in the long-wave infrared region (LWIR) of the electromagnetic spectrum. We derive a general class of TES algorithms [subspace-based TES (SBTES)] relying on the assumption that the emissivity spectra of natural and man-made materials can be well represented in a given subspace of the original data space. Specifically, by exploiting the subspace representation and the Gaussian model for the noise affecting LWIR hyperspectral data, we approach TES under a statistical perspective by obtaining the maximum likelihood estimates of both the temperature and the spectral emissivity. The proposed approach originates several algorithms whose specific form depends on the particular basis matrix adopted to address the emissivity subspace. We study the performance of the presented class of algorithms and derive theoretical bounds on the accuracy of the temperature and emissivity estimators. Furthermore, by specifying two basis matrices for the emissivity subspace, we propose two different algorithms within the SBTES class. Finally, we present the results of an extensive experimental analysis carried out over simulated data to assess and compare the performance of the two presented algorithms
ARTEMIdE - An Automated Underwater Material Recognition Method for Fluorescence LIDAR Invariant to Environmental Conditions
No full textThis article presents an automated underwater material recognition methodology for fluorescence light detection and ranging (LIDAR) invariant to environmental conditions (ARTEMIdE). Contrary to other state-of-the-art methods for submerged object recognition, ARTEMIdE can be applied when no a priori knowledge about environmental conditions is available and without resorting to any additional data besides the received signal and the fluorescence spectral signatures of the materials of interest. Experimental results over synthetic and real data show that ARTEMIdE is effective at automatically recognizing various object materials submerged at different depths within the water column. The presented approach reveals to provide great potential for many marine and submarine applications
CWV-Net: A Deep Neural Network for Atmospheric Column Water Vapor Retrieval from Hyperspectral VNIR Data
Full text linkEstimation of the total column water vapor (CWV) content of the atmosphere plays an important role in the atmospheric compensation (AC) of remotely sensed hyperspectral images collected in the visible and near infrared (VNIR) spectral range. Most of the proposed CWV retrieval methods provide accurate estimates as long as other significant atmospheric parameters are known. Those parameters are not generally available and must in turn be estimated. In this article, a new approach based on deep learning is proposed that allows the estimation of CWV without the knowledge of the atmospheric visibility, the solar zenith angle, and the atmospheric point spread function (PSF). The proposed approach includes a training strategy based on synthetic data that are generated according to an accurate radiative-transfer model, and by exploiting reflectance spectral libraries and the MODTRAN radiative-transfer code. Experiments on simulated data are carried out to analyze the performance of the proposed deep neural network with reference to both aerial and satellite applications. Furthermore, an example of the results provided by the network in a real application is shown. For this purpose, the network is applied to data acquired by an airborne hyperspectral sensor operating in the VNIR spectral range
T cell responses in psoriasis and psoriatic arthritis
No full textAccording to the current view the histological features of psoriasis arise as a consequence of the interplay between T cells, dendritic cells and keratinocytes giving rise to a self-perpetuating loop that amplifies and sustains inflammation in lesional skin. In particular, myeloid dendritic cell secretion of IL-23 and IL-12 activates IL-17-producing T cells, Th22 and Th1 cells, leading to the production of inflammatory cytokines such as IL-17, IFN-γ, TNF and IL-22. These cytokines mediate effects on keratinocytes thus establishing the inflammatory loop.Unlike psoriasis the immunopathogenic features of psoriatic arthritis are poorly characterized and there is a gap in the knowledge of the pathogenic link between inflammatory T cell responses arising in the skin and the development of joint inflammation.Here we review the knowledge accumulated over the years from the early evidence of autoreactive CD8 T cells that was studied mainly in the years 1990s and 2000s to the recent findings of the role of Th17, Tc17 cells and γδ T cells in psoriatic disease pathogenesis. The review will also focus on common and distinguishing features of T cell responses in psoriatic plaques and in synovial fluid of patients with psoriatic arthritis.The integration of this information could help to distinguish the role played by T cells in the initiation phase of the disease from the role of T cells as downstream effectors sustaining inflammation in psoriatic plaques and potentially leading to disease manifestation in distant joints
T Helper Cell Subsets in Clinical Manifestations of Psoriasis
No full textPsoriasis is a chronic inflammatory skin disease, which is associated with systemic inflammation and comorbidities, such as psoriatic arthritis and cardiovascular diseases. The autoimmune nature of psoriasis has been established only recently, conferring a central role to epidermal CD8 T cells recognizing self-epitopes in the initial phase of the disease. Different subsets of helper cells have also been reported as key players in the psoriasis pathogenesis. Here, we reviewed the knowledge on the role of each subset in the psoriatic cascade and in the different clinical manifestations of the disease. We will discuss the role of Th1 and Th17 cells in the initiation and in the amplification phase of cutaneous inflammation. Moreover, we will discuss the recently proposed role of tissue resident Th22 cells in disease memory in sites of recurrent psoriasis and the possible involvement of Th9 cells. Finally, we will discuss the hypothesis of a link between T helper cell subsets recirculating from the skin and the systemic manifestations of psoriasis
- …
