The airborne visible/infrared imaging spectrometer (AVIRIS)

Vane, Gregg; Green, Robert O.; Chrien, Thomas G.; Enmark, Harry T; Hansen, Earl G.; Porter, Wallace M.

doi:10.1016/0034-4257(93)90012-m

Cited by 613 publications

(258 citation statements)

References 6 publications

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“…It was provided by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) [13]. The 20-m GSD data was acquired over the Indian Pine Test Site in Northwestern Indiana in 1992.…”

Section: Methodsmentioning

confidence: 99%

Filter Banks for Hyperspectral Pixel Classification of Satellite Images

Rajadell

García-Sevilla

Pla

2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Satellite hyperspectral imaging deals with heterogenous images containing different texture areas. Filter banks are frequently used to characterize textures in the image performing pixel classification. This filters are designed using different scales and orientations in order to cover all areas in the frequential domain. This work is aimed at studying the influence of the different scales used in the analysis, comparing texture analysis theory with hyperspectral imaging necessities. To pursue this, Gabor filters over complex planes and opponent features are taken into account and also compared in the feature extraction process.

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“…It was provided by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) [13]. The 20-m GSD data was acquired over the Indian Pine Test Site in Northwestern Indiana in 1992.…”

Section: Methodsmentioning

confidence: 99%

Filter Banks for Hyperspectral Pixel Classification of Satellite Images

Rajadell

García-Sevilla

Pla

2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…The hyperspectral data chosen for the experimental results of our visualization technique are from the National Aeronautics and Space Administration Jet Propulsion Laboratory AVIRIS system [37], which captures 224 spectral bands, ranging from 400 to 2500 nm with a 1995 AVIRIS. The image size is usually around 512 × 614 pixels.…”

Section: B Hyperspectral Image Usedmentioning

confidence: 99%

A Multiresolution Markovian Fusion Model for the Color Visualization of Hyperspectral Images

Mignotte

2010

IEEE Trans. Geosci. Remote Sensing

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Abstract-In this paper, we present a nonstationary Markov random field (MRF) fusion model for the color display of hyperspectral images. The proposed fusion or dimensionality reduction model is derived from the preservation of spectral distance criterion. This quantitative metric of good dimensionality reduction and meaningful visualization allows us to derive an appealing fusion model of high-dimensional spectral data, expressed as a Gibbs distribution or a nonstationary MRF model defined on a complete graph. In this framework, we propose a computationally efficient coarse-to-fine conjugate-gradient optimization method to minimize the cost function related to this energy-based fusion model. The experiments reported in this paper demonstrate that the proposed visualization method is efficient (in terms of preservation of spectral distances and discriminality of pixels with different spectral signatures) and performs well compared to the best existing state-of-the-art multidimensional imagery color display methods recently proposed in the literature.Index Terms-Color display, complete graph, conjugategradient method, multidimensional imagery, multiresolution optimization, nonlocal Markov model, nonstationary Markov random field (MRF), visualization of hyperspectral images.

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“…The term spectroscopy was first used in the late 19th century and provides the empirical foundations for atomic and molecular physics (Born and Wolf, 1999). Significant achievements in imaging spectroscopy are attributed to airborne instruments, particularly arising in the early 1980s and 1990s (Goetz et al, 1985;Gower et al, 1987;Green et al, 1998;Kruse et al, 1990;Rowlands et al, 1994;Vane et al, 1984Vane et al, , 1993. However, it was not before 1999 until the first launch of an imaging spectrometer in space (e.g., NASA Moderate Resolution Imaging Spectrometer (MODIS); http://modis.gsfc.nasa.gov/).…”

Section: Brief History Of Spectroscopymentioning

confidence: 99%

Spectrodirectional remote sensing: From pixels to processes

Schaepman

2007

International Journal of Applied Earth Observation and Geoinfor

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This paper discusses the historical evolution of imaging spectroscopy in Earth observation as well as directional (or multiangular) research leading to current achievements in spectrodirectional remote sensing. It elaborates on the evolution from two separate research areas into a common approach to quantify the interaction of light with the Earth surface. The contribution of spectrodirectional remote sensing towards an improved understanding of the Earth System is given by discussing the benefits of converging from individual pixel analysis to process models in the land-biosphere domain. The paper concludes with an outlook of research focus and upcoming areas of interest emphasizing towards multidisciplinary approaches using integrated system solutions based on remote and in situ sensing, data assimilation, and state space estimation algorithms. Abstract This paper discusses the historical evolution of imaging spectroscopy in Earth observation as well as directional (or multiangular) research leading to current achievements in spectrodirectional remote sensing. It elaborates on the evolution from two separate research areas into a common approach to quantify the interaction of light with the Earth surface. The contribution of spectrodirectional remote sensing towards an improved understanding of the Earth System is given by discussing the benefits of converging from individual pixel analysis to process models in the land-biosphere domain. The paper concludes with an outlook of research focus and upcoming areas of interest emphasizing towards multidisciplinary approaches using integrated system solutions based on remote and in situ sensing, data assimilation, and state space estimation algorithms. #

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The airborne visible/infrared imaging spectrometer (AVIRIS)

Cited by 613 publications

References 6 publications

Filter Banks for Hyperspectral Pixel Classification of Satellite Images

Filter Banks for Hyperspectral Pixel Classification of Satellite Images

A Multiresolution Markovian Fusion Model for the Color Visualization of Hyperspectral Images

Spectrodirectional remote sensing: From pixels to processes

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