&lt;title&gt;HYDICE postflight data processing&lt;/title&gt;

208

Abstract-A new approach to multispectral and hyperspectral image analysis is presented. This method, called convex cone analysis (CCA), is based on the fact that some physical quantities such as radiance are nonnegative. The vectors formed by discrete radiance spectra are linear combinations of nonnegative components, and they lie inside a nonnegative, convex region. The object of CCA is to find the boundary points of this region, which can be used as endmember spectra for unmixing or as target vectors for classification. To implement this concept, we find the eigenvectors of the sample spectral correlation matrix of the image. Given the number of endmembers or classes, we select as many eigenvectors corresponding to the largest eigenvalues. These eigenvectors are used as a basis to form linear combinations that have only nonnegative elements, and thus they lie inside a convex cone. The vertices of the convex cone will be those points whose spectral vector contains as many zero elements as the number of eigenvectors minus one. Accordingly, a mixed pixel can be decomposed by identifying the vertices that were used to form its spectrum. An algorithm for finding the convex cone boundaries is presented, and applications to unsupervised unmixing and classification are demonstrated with simulated data as well as experimental data from the hyperspectral digital imagery collection experiment (HYDICE).Index Terms-Classification, convex cone analysis, hyperspectral digital imagery collection experiment (HYDICE), hyperspectral image, multispectral image, unmixing.

Section: B Classification Simulationsmentioning

confidence: 99%

“…The HYDICE sensor [24], [25] was designed to investigate the utility of hyperspectral imaging technology for military and civil applications. The sensor is operated from an aircraft in a pushbroom fashion, oriented at nadir, with 320 spatial pixels per line and 210 spectral bands created by dispersive optics.…”

Section: Application Of Cca To Experimental Datamentioning

confidence: 99%

Multispectral and hyperspectral image analysis with convex cones

Ifarraguerri

Chang

1999

208

“…The HYDICE sensor is a VNIR-SWIR pushbroom imaging spectrometer with wavelength range of 0.4-2.5 μm with 210 spectral bands (nominally 10-nm spectral sampling) [33]- [36]. An on-board illumination source is used to perform an absolute calibration producing data with spectral radiance units.…”

Section: A Absolutely Calibrated Hydice Datamentioning

confidence: 99%

Application of Model-Based Change Detection to Airborne VNIR/SWIR Hyperspectral Imagery

Meola

Eismann

Moses

et al. 2012

Hyperspectral change detection (HSCD) provides an avenue for detecting subtle targets in complex backgrounds. Complicating the problem of change detection is the presence of shadow, illumination, and atmospheric differences, as well as misregistration and parallax error, which often produce the appearance of change. Recent development of a model-based (MB) approach to HSCD has demonstrated potential improvement for mitigating false alarms due specifically to shadow differences using calibrated data. Further development and application of the MB approach is provided here. The method is extended for use on both uncalibrated and relatively calibrated hyperspectral data and is applied to airborne hyperspectral imagery collected using the Hyperspectral Digital Imagery Collection Experiment visible to short-wave infrared sensor and uncalibrated tower imagery collected by the Air Force Research Laboratory.

“…Publisher Item Identifier S 0196-2892(99) 07855-9. output to absolute spectral radiance to allow spectral measurements to be directly related to physical variables [1], [5], [15]. The high-spectral dimensionality of imaging-spectrometer data provides the opportunity to discriminate many materials that cannot be discriminated using sensors with fewer spectral bands [13].…”

Section: Introductionmentioning

confidence: 99%

Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions

Healey

Slater

1999

212

Abstract-The spectral radiance measured by an airborne imaging spectrometer for a material on the Earth's surface depends strongly on the illumination incident of the material and the atmospheric conditions. This dependence has limited the success of material-identification algorithms that rely on hyperspectral image data without associated ground-truth information. In this paper, we use a comprehensive physical model to show that the set of observed 0.4-2.5 m spectral-radiance vectors for a material lies in a low-dimensional subspace of the hyperspectral-measurement space. The physical model captures the dependence of the reflected sunlight, reflected skylight, and path-radiance terms on the scene geometry and on the distribution of atmospheric gases and aerosols over a wide range of conditions. Using the subspace model, we develop a local maximum-likelihood algorithm for automated material identification that is invariant to illumination, atmospheric conditions, and the scene geometry. The algorithm requires only the spectral reflectance of the target material as input. We show that the low dimensionality of material subspaces allows for the robust discrimination of a large number of materials over a wide range of conditions. We demonstrate the invariant algorithm for the automated identification of material samples in HYDICE imagery acquired under different illumination and atmospheric conditions.