Detecting onshore hydrocarbon is a major topic for both environmental monitoring and exploration. In this work, a hyperspectral image acquired nearby an old oil extraction site in tropical area is analyzed. The area of interest includes a pit filled with bio-degraded heavy oil, surrounded by herbaceous vegetation and many lagoons.
First, we focused on methodologies that can detect oil pollution in an unsupervised manner. Based on the assumption that such oil pits are rare events in the image, statistical approach for anomalies detection, derived from the Reed-Xiaoli detector, is used. In order to decrease the number false alarms, some a priori knowledge about the spectral signature of the pits and about the background is introduced. This approach succeeds in detecting the pit with very few false alarms.
Hydrocarbon pollution can have an impact on vegetation and leads to change in vegetation (bio)physical parameters (pigments, water content, …), according to species, pollutant type and exposition time . In order to map the polluted area without any a priori knowledge, several un-supervised classification, including an original method of automatic classification combining unmixing approach and SVM (support Vector Machine) are applied and compared. The results are compared with a partial “ground truth map” that has been derived from visual observations on the field, and with areas of stressed vegetation that have been mapped using combination of specific spectral indices. The classification results are consistent with the ground truth map and the retrieved stressed vegetation areas.
Seven countries within the European Defence Agency (EDA) framework are joining effort in a four year project (2009-2013) on Detection in Urban scenario using Combined Airborne imaging Sensors (DUCAS). Data has been collected in a joint field trial including instrumentation for 3D mapping, hyperspectral and high resolution imagery together with in situ instrumentation for target, background and atmospheric characterization. Extensive analysis with respect to detection and classification has been performed. Progress in performance has been shown using combinations of hyperspectral and high spatial resolution sensors.
Anomaly detection (AD) in hyperspectral data has received a lot of attention for various applications. The aim
of anomaly detection is to detect pixels in the hyperspectral data cube whose spectra differ significantly from
the background spectra. Many anomaly detectors have been proposed in literature. They differ by the way
the background is characterized and by the method used for determining the difference between the current
pixel and the background. The most well-known anomaly detector is the RX detector that calculates the
Mahalanobis distance between the pixel under test (PUT) and the background. Global RX characterizes the
background of the complete scene by a single multi-variate normal distribution. In many cases this model is not
appropriate for describing the background. For that reason a variety of other anomaly detection methods have
been developed. This paper examines three classes of anomaly detectors: sub-space methods, local methods
and segmentation-based methods. Representative examples of each class are chosen and applied on a set of
hyperspectral data with different backgrounds. The results are evaluated and compared.
In this paper, we propose an innovative classification method dedicated to hyperspectral images which uses
both spectral information (Principal Component Analysis bands, Minimum Noise Fraction bands) and spatial
information (textural features and segmentation). The process includes a segmentation as a pre-processing step,
a spatial/spectral features calculation step and finally an area-wise classification. The segmentation, a region
growing method, is processed according to a criterion called J-image which avoids the risks of over-segmentation
by considering the homogeneity of an area at a textural level as well as a spectral level. Then several textural
and spectral features are calculated for each area of the segmentation map and these areas are classified with
a hierarchical ascendant classification. The method has been applied on several data sets and compared to the
Gaussian Mixture Model classification. The JSEG classification process finally appeared to gives equivalent, and
most of the time more accurate classification results.
Infrared hyperspectral imagery gives new opportunities for night observations for military, or security purposes, and for
geological studies as rocks have specific infrared absorption bands. Generally, an optimized utilization of spectral
information requires to retrieve spectral emissivity, which involves atmospheric compensation and surface temperature
and emissivity separation (TES). This paper presents a new method dedicated to a future airborne hyperspectral sensor
that will operate in the 3-5.5 and 8-12 µm spectral ranges, at 2.2 km height. It combines neural networks in order to
characterize the required parameters for atmospheric compensation and a spectral smoothness approach for TES. The
network training is performed with radiance spectra simulated with MODTRAN4, and using ASTER emissivities, and
the TIGR atmospheric database. A sensitivity study based on experimental design is carried out in order to compare
impacts of atmospheric and surface parameters on radiance at several wavelengths. Atmospheric compensation and TES
methods are then presented and their accuracy is assessed. Sensitivity of the retrievals to instrumental characteristics
such as signal to noise ratio and radiometric calibration, is also studied.
The retrieval of surface emissivity and temperature from infrared radiances measured by an airborne hyperspectral sensor closely depends on the ability to correct the acquired data from atmospheric effects. In this paper we present a new atmospheric correction scheme based on sounding techniques and neural networks. A key problem of neural network is to select relevant entries and outputs. Therefore, a preliminary sensitivity analysis that takes into account atmospheric conditions as well as the surface emissivity and temperature variations is carried out. It shows that only the first three or four PCA coefficients of atmospheric profiles have a significant influence on the radiance measured in the 4.26 μm carbon dioxide and the 6.7 μm water absorption bands. But these coefficients allow to rebuilt temperature and water profiles with enough accuracy for the addressed problem. This lead us to develop two groups of neural networks, the first one to estimate the main PCA coefficients of temperature profile, and the second one to retrieve the related water PCA coefficients. The atmospheric profiles thus obtained are then used to derive the "ground" radiances. Eventually we evaluate the accuracy of surface temperature and emissivity obtained with the derived atmospheric profiles.
Spectral unmixing decomposes an hyperspectral image into a collection of reflectance spectra of the macroscopic materials present in the scene, called endmembers, and the corresponding abundance fractions of these constituents. The purpose of this paper is to compare the performance of several algorithms that process unsupervised endmember extraction from hyperspectral images in the visible and NIR spectral ranges. After giving an analytical formulation of the observations, two significantly different approaches have been described. The first one exploits convex geometry the problem answers to. The second one is based on statistical principles of Independent Component Analysis, which is a classical resolution of the Blind Source Separation issue. First, the performance of the algorithms are compared on synthetic images and sensibility to noise is studied. Then the best methods are applied on part of a HyMap image.
Hyperspectral imagery provides detailed spectral information on the observed scene which enhances detection possibility, in particular for subpixel targets. In this context, we have developed and compared several anomaly detection algorithms based on a projection pursuit approach. The projection pursuit is performed either on the ACP or on the MNF (Minimum Noise Fraction) components. Depending on the method, the best axes of the eigenvectors basis are directly selected, or a genetic algorithm is used in order to optimize the projections. Two projection index (PI) have been tested: the kurtosis and the skewness. These different approaches have been tested on Aviris and Hymap hyperspectral images, in which subpixel targets have been included by simulation. The proportion of target in pixels varies from 50% to 10% of the surface. The results are presented and discussed. The performance of our detection algorithm is very satisfactory for target surfaces until 10% of the pixel.
A new model has been developed to estimate irradiance at ground level over a rugged terrain in the reflective spectral domain in order to be used in an hyperspectral inversion code. Modtran4 allows to calculate atmospheric parameters over a flat scene which are then used to estimate the four components of irradiance over a mountainous area (direct, diffuse, reflected and coupling irradiance). This method have been compared with an accurate radiative transfer code called AMARTIS. Simulations are done at three wavelengths and for two solar configurations over a relief composed of two hills and flat terrain. Irradiances obtained with our model are in good agreement with this reference code except in shadow areas in the SWIR. Our model is also compared with a currently used model developed by Sandmeier whose results are worse than our model's results. Current relative errors of our diffuse, reflected and coupling irradiance calculation model do not have much influence on total irradiance in most of the cases. This influence become significant for high beam incidence angles where Digital Elevation Model errors can be much more important.
The Optics Department of ONERA has developed and implemented an inverse algorithm, COSHISE, to correct hyperspectral images of the atmosphere effects in the visible-NIR-SWIR domain (0,4-2,5 micrometers ). This algorithm automatically determine the integrated water-vapor content for each pixel, from the radiance at sensor level by using a LIRR-type (Linear Regression Ratio) technique. It then retrieves the spectral reflectance at ground level using atmospheric parameters computed with Modtran4, included the water-vapor spatial dependence as obtained in the first stop. The adjacency effects are taken into account using spectral kernels obtained by two Monte-Carlo codes. Results obtained with COCHISE code on real hyperspectral data are first compared to ground based reflectance measurements. AVIRIS images of Railroad Valley Playa, CA, and HyMap images of Hartheim, France, are use. The inverted reflectance agrees perfectly with the measurement at ground level for the AVIRIS data set, which validates COCHISE algorithm/ for the HyMap data set, the results are still good but cannot be considered as validating the code. The robustness of COCHISE code is evaluated. For this, spectral radiance images are modeled at the sensor level, with the direct algorithm COMANCHE, which is the reciprocal code of COCHISE. The COCHISE algorithm is then used to compute the reflectance at ground level from the simulated at-sensor radiance. A sensitivity analysis has been performed, as a function of errors on several atmospheric parameter and instruments defaults, by comparing the retrieved reflectance with the original one. COCHISE code shows a quite good robustness to errors on input parameter, except for aerosol type.
Vertical concentration profiles of three trace species, HCl, CO, and NO, in the middle atmosphere have been retrieved from solar occultation infrared spectra recorded by the Grille spectrometer during the ATLAS-1 mission with a spectral resolution of about 0.1 cm-1. HCl and NO profiles are compared with ATMOS interferometer measurements obtained during the ATLAS-1 mission, and with HALOE (Halogen Occultation Experiment on the Upper Atmosphere Research Satellite) measurements performed over the same period (24 March - 1 April 1992) and latitude range. A large increase in HCl abundance is estimated by comparison of HCl profiles produced by the Grille spectrometer with profiles obtained from the ATMOS interferometer during the 1985 Spacelab-3 mission. The estimated increase rate is in accordance with the rate deduced from ATMOS measurements performed in 1992 during the same mission ATLAS-1, and with model predictions. NO mixing ratios are in general agrement with HALOE and ATMOS profiles and reported measurements from other instruments. CO profiles extend up to 100-110 km and show a large vertical gradient in CO volume mixing ratio in the mesosphere and the lower part of the thermosphere, as expected by chemical models and observed by various instruments.
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