Possibilities for the study of the NLTE effect on atmospheric CO<inf>2</inf> spectral signatures induced by a blue jet using an infrared spectro-imager embedded in a stratospheric balloon
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HALESIS (High Altitude Luminous Events Studied by Infrared Spectro-imagery) is an innovative project based on hyperspectral imagery. The purpose of this experience is to measure the atmospheric perturbation in the minutes following the occurrence of Transient Luminous Events (TLEs) from a stratospheric balloon in the altitude range of 20 to 40 km. The first part of the study has been dedicated to establish the project feasibility. To do that, we have simulated spectral perturbations induced by an isolated blue jet. Simulations have been performed using the line by line radiative transfer model LBLRM taking into account the Non Local Thermodynamic Equilibrium. The case of the estimation of the CO 2 infrared signature that could be the result of a single blue jet occurrence is presented. Then, the estimated spectral signatures have been confronted with the technical capabilities of different kinds of hyperspectral imagers. The study has demonstrated the feasibility of the project, but it has also pointed to the challenges to build perfectly adapted instrument.Keywords:
Spectral signature
In hyperspectral imaging, pixels of interest generally incorporate information from disparate components which requires quantitative decomposition of these pixels to extract desired information. Since hyperspectral sensors collect data in hundreds of spectral bands, it is essential to perform spectral unmixing to identify the spectra of all endmembers in the pixel in order to ascertain the fractional abundances of pure target spectral signatures. By extracting desired spectral signature from high-dimensional remotely sensed hyperspectral imagery, one can detect and identify objects in vast geographical regions. While numerous algorithms were developed for target detection in hyperspectral imagery, a unified and synergistic approach to evaluate the performance of these algorithms for oil spill detection in ocean environment is yet to be done. Consequently, in this paper, we investigate and compare the performance of five most widely used target detection algorithms for the identification and tracking of surface and subsurface oil spills in ocean environment. Test results using real life oil spill based hyperspectral image datasets show that the spectral fringe-adjusted joint transform correlation technique and the constrained energy minimization technique yield better results compared to alternate techniques.
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Hyperspectral imaging allows for analysis of images in several hundred of spectral bands depending on the spectral resolution of the imaging sensor. Hyperspectral document image is the one which has been captured by a hyperspectral camera so that the document can be observed in the different bands on the basis of their unique spectral signatures. To detect the forgery in a document various Ink mismatch detection techniques based on hyperspectral imaging have presented vast potential in differentiating visually similar inks. Inks of different materials exhibit different spectral signature even if they have the same color. Hyperspectral analysis of document images allows identification and discrimination of visually similar inks. Based on this analysis forensic experts can identify the authenticity of the document. In this paper an extensive ink mismatch detection technique is presented which uses KMean Clustering to identify different inks on the basis of their unique spectral response and separates them into different clusters.
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Hyperspectral imaging sensors measure the radiance of the materials within each pixel
area at a very large number of contiguous spectral wavelength bands. So, they can generate
hundreds of images of a scene on the real surface. The radiance is converted into
hyperspectral data cube digital form. The spectral information available in a hyperspectral
image (cube) may serve to classify the nature of the target object because every material
had a unique fixed spectrum and could be used as a spectral signature of the material and
perhaps provide additional information for further processing and exploitation. Hyperspectral
data contain extremely rich spectral attributes, which offer the potential to discriminate
more detailed classes with classification accuracy.
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Variable illumination and environmental, atmospheric, and temporal conditions cause the measured spectral signature for a material to vary within hyperspectral imagery. By ignoring these variations, errors are introduced and propagated throughout hyperspectral image analysis. To develop accurate spectral unmixing and endmember estimation methods, a number of approaches that account for spectral variability have been developed. This article motivates and provides a review for methods that account for spectral variability during hyperspectral unmixing and endmember estimation and a discussion on topics for future work in this area.
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The spectral signatures in most hyperspectral classification approaches are generally treated as random vectors, which is inappropriate in denoting their typical physical characteristics, such as central wavelengths, widths, and depths of absorption bands. In this paper, we present a new classification approach by enhancing the absorption bands of spectral signatures to boost their physical information. Firstly, an analysis is made of the characteristics of absorption bands of spectral signatures. Next, an absorption bands enhancing approach is proposed based on the discussion of the approach of fusing spectral signatures and their derivative. Finally, the proposed approach is applied on two real hyperspectral subimages. The experimental results show that our proposed approach can significantly enhance the differences of spectral signatures of a hyperspectral images. And thus can improve the classification performance of hyperspectral images.
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Spectral libraries are required for successful classification because each pure material has its own unique spectral signature which are essential for the classification of hyperspectral imagery. Each pixel within a hyperspectral image naturally contains more than one material; thus, determining the spectral mixture of pixels, also known as the spectral unmixing problem, is critical. Moreover, spectral signatures vary according to species, so that it is critical for Turkey to develop its own spectral library for indigenous plant varieties. It is estimated that there are over 200 species indigenous to Anatolia. Without a geography specific library, the results of hyperspectral image classification and spectral unmixing will be erroneous - i.e., results obtained with use of non-native libraries such as that of the USGS can yield flawed results.
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The objective of the US Army Hyperspectral Mine Detection Phenomenology program was to determine if spectral discriminants exist that are useful for the detection of land mines. Statistically significant mine signature data were collected over a wide spectral range and analyzed to identify robust spectral features that might serve as discriminants for new airborne sensor concepts. Detection metrics which characterize the deductibility of land miens and which predict the detection performance of a general class of hyperspectral detection algorithms were selected and applied. Detection performance of land mines was analyzed against background type, age of buried miens and possible sensor design parameters. This paper describes the result of this analysis and present EO/IR hyperspectral sensor and algorithm design concepts that could potentially be used to operationally detect buried land mines.
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The ability to accurately detect a target of interest in a hyperspectral imagery (HSI) is largely dependent on the spatial and spectral resolution. While hyperspectral imaging provides high spectral resolution, the spatial resolution is mostly dependent on the optics and distance from the target. Many times the target of interest does not occupy a full pixel and thus is concealed within a pixel, i.e. the target signature is mixed with other constituent material signatures within the field of view of that pixel. Extraction of spectral signatures of constituent materials from a mixed pixel can assist in the detection of the target of interest. Hyperspectral unmixing is a process to identify the constituent materials and estimate the corresponding abundances from the mixture. In this paper, a framework based on non-negative matrix factorization (NMF) is presented, which is utilized to extract the spectral signature and fractional abundance of human skin in a scene. The NMF technique is employed in a supervised manner such that the spectral bases of each constituent are computed first, and then these bases are applied to the mixed pixel. Experiments using synthetic and real data demonstrate that the proposed algorithm provides an effective supervised technique for hyperspectral unmixing of skin signatures.
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Anomaly detection in hyperspectral images using multi-temporal data is useful in applications like crop monitoring and military surveillance. Spatial resolution of hyperspectral image is in the order of 4 × 4 m to 20 × 20 m, so one pixel may contain more than one material. The spectral signature of an image at given pixel may be mixing of spectral signatures of more than one materials. Hyperspectral unmixing is the process to determine number of materials present in mixed pixel, the spectral signatures of mixing materials and their fractional proportion. Hyperspectral unmixing enables variety of applications like anomaly detection, change detection, mineral exploitation and manmade material identification and detection, and target detection. Hyperspectral unmixing involves two steps, first estimates the spectral signature of pure material presents in image, known as endmembers and second determines their proportion in mixed pixels, known as abundances. Vertex Component Algorithm (VCA) is a fast and powerful algorithm. It determines the endmember signature with the assumption of presence of one pure pixel per endmember present in hyperspectral image. The paper discusses about the anomaly detection present in hyperspectral image using Vertex Component Analysis algorithm. Simulation results for anomaly detection using multi temporal hyperspectral data are discussed. Synthetically multi-temporal data sets are generated for synthetic data sets as well as for real cuprite image.
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Hyperspectral imaging is an emerging modern technique in modern remote sensing that expands and removes capability of multispectral image analysis. It takes advantage of hundreds of continuous spectral channels to uncover materials that usually cannot be resolved by multispectral sensoThis book is a collection of research papers of Indian scientist working in the field of hyperspectral remote sensing and spectral signature applications. This has been organized in a way that all the s are logically connected and can be referred back and the forth one another for more details. The title of “hyperspectral remote sensing and spectral signature applications” is use to reflect its focus on spectral techniques, i.e. non-literal techniques that are especially designed and developed for hyperspectral imagery rather than multispectral imagery.
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