1,721,039 research outputs found
Improved Learning-Based Approach for Atmospheric Compensation of VNIR-SWIR Hyperspectral Data
Full text linkIn this work, the Learning-Based Approach to Atmospheric Compensation (LBAC) of hyperspectral data proposed by Acito et al. is extended. LBAC makes use of machine learning methods to directly estimate the spectral reflectance from the at-sensor radiance accounting for the variability induced by one or more unknown atmospheric parameters and by-passing their estimation. LBAC training is obtained by exploiting a spectral reflectance library and accounting for the effects of both the atmosphere and the noise. However, depending on the spectral library adopted, some specific spectra may be reconstructed with lower accuracy. To overcome this drawback, two solutions are proposed referring to two application scenarios. The former deals with small and rare anomalous pixels with unknown reflectance and could be of interest in many applications such as man-made targets detection. It leverages the strengths of LBAC and those of the empirical line method (ELM). The second scenario refers to the case of materials with a priori known spectral reflectance and is defined for applications such as mining exploration and contaminant detection. It directly acts on the training phase of LBAC by introducing the spectra of interest in the generation of the training set. An extensive analysis is carried out on simulated data to test the effectiveness of the proposed solutions, to discuss their strengths and weakness, and to compare them with a classical physics-based approach. Results on a real hyperspectral image acquired by an airborne sensor provide a demonstration of the effectiveness of the proposed strategies in a real application environment
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
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
Hyper-spectral data modelling by non-Gaussian statistical distributions
No full textIn this manuscript we investigate on the statistical
modeling of hyper-spectral data. Accurately modeling real data is
of paramount importance in the design of optimal classification
or detection strategies and in evaluating their performances. In
the work three non-Gaussian models are considered and their
capability in characterizing the statistical behavior of real data is
discussed with reference to a data set acquired by the
Multispectral Infrared and Visible Imaging Spectrometer
(MIVIS) sensor
Adaptive detection algorithm for full pixel targets in hyperspectral images
No full textInfrared surveillance systems have the task of detecting small moving targets having low signal-to-clutter ratio. Detection is usually accomplished by (1) removing the background structures from each frame and (2) integrating the target signal over consecutive frames of the residual sequence. We focus on the analysis of background removal techniques based on linear and nonlinear two-dimensional filters such as the window average, median, max-median, and max-mean. We introduce two modified versions of the window average and max-mean filters, where an appropriate guard window is used to reduce the bias due to the target. We define an ad hoc methodology to compare the different background estimation techniques on the basis of their ability to suppress background structures and to preserve the target of interest. Finally, we present and discuss the results obtained over two experimental IR sequences containing a highly structured background. (c) 2005 Society of Photo-Optical Instrumentation Engineers
Hyperspectral signal subspace identification in the presence of rare signal components
No full textIn this paper, we investigate the problem of signal subspace identification (SSI) and dimensionality reduction in hyperspectral images. We consider two recently proposed SSI algorithms: the Maximum Orthogonal Complement Analysis (MOCA) algorithm and the Robust Signal Subspace Estimator (RSSE) algorithm. Such algorithms are robust to the presence of rare signal components and are particularly effective in reducing the number of features in the preprocessing step for small target detection applications. In this paper, MOCA and RSSE are briefly revisited and integrated in a common theoretical framework in order to better highlight and understand their peculiarities. Furthermore, their performances are compared in terms of computational complexity and of their ability to address both the abundant and the rare signal components. A modified version of the MOCA is also introduced, which is computationally more efficient than the original algorithm. Results on simulated data are discussed, and a case study is presented concerning real Airborne Visible/Infrared Imaging Spectrometer data
Computational Load Reduction for Anomaly Detection in Hyperspectral Images: An Experimental Comparative Analysis
No full textIn this manuscript we investigate the efficient implementation of anomaly detection strategies in hyperspectral images. We especially focus on methods to reduce the computational complexity for a fast implementation of the detection algorithms. In particular, we consider two strategies based on data fusion methods applied to the outputs of the optical heads of the hyperspectral sensor. Furthermore, we consider, two computationally efficient implementations of anomaly detection where the well known RX algorithm is applied to hyperspectral data after dimensionality reduction. The detection performances of the anomaly detection strategies are compared using real data acquired by the MIVIS sensor. An estimate of the reduction of the computational load achieved with the different techniques is also provided
On the CFAR property of the RX algorithm in the presence of signal dependent noise in hyperspectral images
No full textIn this paper, we investigate the constant false-alarm rate (CFAR) property of the RX anomaly detector which is widely used for the analysis of hyperspectral data. The RX detector relies on an adaptive scheme where the mean vector and the covariance matrix of the background are locally estimated from the image pixels themselves. First, demeaning is accomplished by removing the estimated local background mean value, and then, the covariance matrix is estimated in a homogeneous neighborhood of each pixel. In principle, if the local mean is perfectly removed and the covariance matrix is estimated from background pixels sharing the same covariance matrix, the RX algorithm has the CFAR property, which is highly desirable in practical applications. The CFAR behavior of the algorithm also requires the spatial stationarity of the random noise affecting the hyperspectral image. In data collected by new-generation sensors, such an assumption is not valid because photon noise contribution, which depends on the spatially varying signal level, is not negligible. This has motivated us to analyze the behavior of the RX algorithm with respect to the CFAR property in data affected by signal-dependent (SD) noise. In this paper, we show both theoretically and experimentally that the SD noise is one of the causes of the non-CFAR behavior of the RX detector that we have experienced in many practical situations. We propose a strategy to enhance the robustness of the anomaly detection scheme with respect to the CFAR property based on an adaptive nonlinear transform aimed at reducing the dependence of the noise on the signal level. Experiments on simulated data and real data collected by a new hyperspectral camera are also presented and discussed
Detection Peformance Loss due to Jitter in Naval IRST Systems
No full textInfrared search and track (IRST) systems based on 3-D
velocity filters are quite sensitive to the random motion that
remains after egomotion compensation has occurred (jitter).
These systems are designed to detect small targets at long range
and they often employ track-before-detect (TBD) strategies to
integrate the target signal along consecutive frames. Jitter causes
frame misalignment and leads to a reduction of the detection
performance. We analyze the effects of jitter on the false alarm
and detection probabilities and on the detection range. The
analytical expressions here derived are a valuable tool both in
the analysis and synthesis of IRST systems based on 3-D velocity
filters
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