This paper puts forward a methodology developed at the Institut Cartogràfic i Geològic de Catalunya (ICGC) to quantify
upwelling light flux using hyperspectral and photogrammetric airborne data. The work was carried out in the frame of a
demonstrative study requested by the municipality of Sant Cugat del Vallès, in the vicinity of Barcelona (Spain), and
aimed to envisage a new approach to assess artificial lighting policies and actions as alternative to field campaigns.
Hyperspectral and high resolution multispectral/panchromatic data were acquired simultaneously over urban areas. In order
to avoid moon light contributions, data were acquired during the first days of new moon phase. Hyperspectral data were
radiometrically calibrated. Then, National Center for Environmental Prediction (NCEP) atmospheric profiles were
employed to estimate the actual Column Water Vapor (CWV) to be passed to ModTran5.0 for the atmospheric
transmissivity τ calculation. At-the-ground radiance was finally integrated using the photopic sensitivity curve to generate
a luminance map (cdm-2) of the flown area by mosaicking the different flight tracks. In an attempt to improve the spatial
resolution and enhance the dynamic range of the luminance map, a sensor-fusion strategy was finally looked into. DMC
Photogrammetric data acquired simultaneously to hyperspectral information were converted into at-the-ground radiance
and upscaled to CASI spatial resolution. High-resolution (HR) luminance maps with enhanced dynamic range were finally
generated by linearly fitting up-scaled DMC mosaics to the CASI-based luminance information. In the end, a preliminary
assessment of the methodology is carried out using non-simultaneous in-situ measurements.
In this paper we present the development of a technique, based on multiresolution wavelet decomposition, for the merging and data fusion of a high-resolution panchromatic image and a low- resolution multispectral image. The method presented here consists of adding the wavelet coefficients of the high- resolution image to the multispectral (low-resolution) data. We have studied several possibilities concluding that the method which produces the best results consists in adding the high order coefficients of the wavelet transform of the panchromatic image to the intensity component (defined as L equals R+G+B/3) of the multispectral image. Using this method, the detail information from both images is preserved. The method is capable of enhancing the spatial quality of the multispectral image while preserving its spectral content to a greater extent. The method presented does not modify the total energy of the multispectral image, since the mean value of each of the added wavelet planes is 0. The method is, thus, an improvement on standard Intensity-Hue-Saturation (IHS or LHS) mergers. We used the method to merge SPOT and LANDSAT (TM) images. The technique presented is clearly better than the IHS and LHS mergers in preserving both spectral and spatial information.
The present work consists on the generation of a DEM using ERS satellites interferometric data over a wide area (50 X 50 Km) with an error study using a high accuracy reference DEM, focusing on the atmosphere induced errors. The area is heterogeneous with flat and rough topography ranging from sea level up to 1200 m in the inland ranges. The ERS image has a 100 X 100 Km2 area and has been divided in four quarters to ease the processing. The phase unwrapping algorithm, which is a combination of region growing and least squares techniques, worked out successfully the rough topography areas. One quarter of the full scene was geocoded over a local datum ellipsoid to a UTM grid. The resulting DEM was compared to a reference one provided by the Institut Cartografic de Catalunya. Two types of atmospheric error or artifacts were found: a set of very localized spots, up to one phase cycle, which generated ghost hills up to 100, and a slow trend effect which added up to 50 m to some areas in the image. Besides of the atmospheric errors, the quality of the DEM was assessed. The quantitative error study was carried out locally at several areas with different topography.
We apply the maximum likelihood estimator (MLE) method to restore scanned photogrammetric plates. In color images, the restoration is carried out separately for each band. To compute the MLE solution, we use the algorithm based on the expectation maximization (EM) algorithm for Poisson data. The memory and CPU time needed to obtain MLE solutions make feasible the restoration of images of 512 X 512 or 1024 X 1024 pixels, but the restoration of a series of large scanned photographs, as happens with photogrammetric plates, is not of practical use with present-day computers. With the aim of bypassing this problem, several 512 X 512 portions of the plate are extracted and restored with the MLE algorithm. Then, the convolution function transforming the original into the restored image is calculated in Fourier space, thus obtaining a convolution matrix when returning to normal space. Next, this matrix is truncated and a convolution is performed over the whole image. A series of tests over the same image digitized with different scanners has been carried out to separate this contribution from environmental effects.
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