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Estimation 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. [ABSTRACT FROM AUTHOR]

This paper deals with atmospheric correction in hyperspectral data acquired in the long-wave infrared (LWIR) spectral range. Atmospheric compensation (AC) is approached from a new perspective in that it is reformulated as an estimation problem on a low-rank subspace. Specifically, a subspace-based model is exploited to represent both the atmospheric transmittance and the upwelling radiance. Taking into account the inherent correlation between these two quantities, we adopt a subspace model that constrains them to vary in a physically consistent way. Exploiting such a model, AC is formulated as a nonlinear least-squares estimation problem. An automatic procedure is proposed to estimate the unknown parameters, which is based only on the image analysis and does not need any atmospheric measurements. Results of an extensive experimental analysis carried out on simulated data are used to discuss the performance of the algorithm with respect to different atmospheric conditions. Finally, experimental evidence over a real LWIR hyperspectral image is provided. [ABSTRACT FROM AUTHOR]

The 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 algorithms is the atmospheric precorrected differential absorption (APDA) technique. 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. 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. [ABSTRACT FROM AUTHOR]