Gotowa bibliografia na temat „Hyperpectral images”

Flexible tools for photogrammetry and remote sensing using unmanned airborne vehicles (UAVs) have been attractive topics of research and development. The lightweight hyperspectral camera based on a Fabry-Pérot interferometer (FPI) is one of the highly interesting tools for UAV based remote sensing for environmental and agricultural applications. The camera used in this study acquires images from different wavelengths by changing the FPI gap and using two CMOS sensors. Due to the acquisition principle of this camera, the interior orientation parameters (IOP) of the spectral bands can vary for each band and sensor and changing the configuration also would change these sets of parameters posing an operational problem when several bands configurations are being used. The objective of this study is to assess the impact of use IOPs estimated for some bands in one configuration for other bands of different configuration the FPI camera, considering different IOP and EOP constraints. The experiments were performed with two FPI-hyperspectral camera data sets: the first were collected 3D terrestrial close-range calibration field and the second onboard of an UAV in a parking area in the interior of São Paulo State.

Flexible tools for photogrammetry and remote sensing using unmanned airborne vehicles (UAVs) have been attractive topics of research and development. The lightweight hyperspectral camera based on a Fabry-Pérot interferometer (FPI) is one of the highly interesting tools for UAV based remote sensing for environmental and agricultural applications. The camera used in this study acquires images from different wavelengths by changing the FPI gap and using two CMOS sensors. Due to the acquisition principle of this camera, the interior orientation parameters (IOP) of the spectral bands can vary for each band and sensor and changing the configuration also would change these sets of parameters posing an operational problem when several bands configurations are being used. The objective of this study is to assess the impact of use IOPs estimated for some bands in one configuration for other bands of different configuration the FPI camera, considering different IOP and EOP constraints. The experiments were performed with two FPI-hyperspectral camera data sets: the first were collected 3D terrestrial close-range calibration field and the second onboard of an UAV in a parking area in the interior of São Paulo State.

Abstract The Ronda Peridotite, south of Andalusia (Spain), was imaged by AVIRIS in 1991 and partially sampled by us in the field with a GER 3700 spectrometer in 1997 in order to get experience in processing hyperpectral images of planetary surfaces with probes such as ISM Phobos (1989), OMEGA Mars Express (2003) and VIMS Cassini (2004). The high spectral resolution of the images (224 channels from 400 to 2455 nm) is necessary to conduct geological analysis with remote petrological determinations of rock types. On Earth, it is also necessary to determine species of vegetation because of their strong influence in mapping lithology, even in dry areas like the Ronda peridotite. The Ronda AVIRIS image was first processed to infer geological features using photo-interpretation of colour composite images extracted from 150 useful channels compared to geological maps and checked on the field during the campaign of July 97. This allows us to distinguish easily the peridotite massif from its surrounding rocks and its own serpentine zoning. Since this work followed the work of Chabrillat et al. [2000] we chose to explore the AVIRIS data with other techniques. We chose to remove the contribution of the atmosphere with spectra collected in the field on a white target at various altitudes and to remove the main vegetation with spectra of the most characteristic vegetation of the peridotite. In both cases we first estimated the amount of atmosphere and vegetation with band ratios and remove them with two similar empiric corrections of the reflectance. From the spectroscopy data, after removal of the atmosphere and some vegetation signal, we were able to clearly distinguish the crustal rocks from the mantle ones, as well as compositional variations due to pyroxene and mostly serpentine abundance within the peridotites. Hyperspectral infrared spectrometry will provide good geological mapping of the main rocks on planetary surfaces, if images can also be calibrated with in situ field measurements which will not miss any unexpected component. However, some ambiguities remain between certain types of rock which have close mineralogical composition (e.g. harzburgite compared to lherzolite) or which have resulting spectra very similar to each other (plagioclase and lizardite in peridotites). Some other ambiguities between spectra are also introduced by techniques of analysis based on relative reflectance. By not taking into account absolute intensity of the reflectance, because of roughness and topographic shading effects, small mineral variations are not always visible.

This paper presents a new factorization technique for hyperspectral signal processing based on a constrained singular value decomposition (SVD) approach. Hyperpectral images typically have a large number of contiguous bands that are highly correlated. Likewise the field of view typically contains a limited number of materials and the spectra are also correlated. Only a selected number of bands, the extreme bands that include the dominant materials spectral signatures, are needed to express the data. Factorization can provide a means for interpretation and compression of the spectral data. Hyperspectral images are represented as non-negative matrices by graphic concatenation, with the pixels arranged into columns and each row corresponding to a spectral band. SVD and principal component analysis enjoy a broad range of applications, including, rank estimation, noise reduction, classification and compression, with the resulting singular vectors forming orthogonal basis sets for subspace projection techniques. A key property of non-negative matrices is that their columns/rows form non-negative cones, with any non-negative linear combination of the columns/rows belonging to the cone. Data sets of spectral images and time series reside in non-negative orthants and while subspaces spanned by SVD include all orthants, SVD projections can be constrained to the non-negative orthants. In this paper we utilize constraint sets that confine projections of SVD singular vectors to lie within the cones formed by the spectral data. The extreme vectors of the cone are found and these vectors form a basis for the factorization of the data. The approach is illustrated in an application to hyperspectral data of a mining area collected by an airborne sensor.