Canada Centre for Remote Sensing

About: Canada Centre for Remote Sensing is a based out in . It is known for research contribution in the topics: Synthetic aperture radar & Radar imaging. The organization has 480 authors who have published 958 publications receiving 42878 citations.

On the Slope-Aspect Correction of Multispectral Scanner Data

Abstract: SUMMARYThe effects of topography on the radiometric properties of multispectral scanner (MSS) data are examined in the context of the remote sensing of forests in mountainous regions. The two test areas considered for this study are located in the coastal mountains of British Columbia, one at the Anderson River near Boston Bar and the other at Gun Lake near Bralorne. The predominant forest type at the former site is Douglas fir, whereas forest types at the latter site are primarily lodgepole pine and ponderosa pine. Both regions have rugged topography, with elevations ranging from 330 to 1100 metres above sea level at Anderson River and from 750 to 1300 metres above sea level at Gun Lake.Lambertian and non-Lambertian illumination corrections are formulated, taking into account atmospheric effects as well as topographic variations. Terrain slope and aspect values are determined from a digital elevation model and atmospheric parameters are obtained from a model atmosphere computation for the solar angles an...

Coherence estimation for SAR imagery

Abstract: In dual- or multiple-channel synthetic aperture radar (SAR) imaging modes, cross-channel correlation is a potential source of information. The sample coherence magnitude is calculated over a moving window to generate a coherence magnitude map. High-resolution coherence maps may be useful to discriminate fine structures. Coarser resolution is needed for a more accurate estimation of the coherence magnitude. In this study, the accuracy of coherence estimation is investigated as a function of the coherence map resolution. It is shown that the space-averaged coherence magnitude is biased toward higher values. The accuracy of the coherence magnitude estimate obtained is a function of the number of pixels averaged and the number of independent samples per pixel (i.e., the coherence map resolution). A method is proposed to remove the bias from the space-averaged sample coherence magnitude. Coherence magnitude estimation from complex (magnitude and phase) coherence maps is also considered. It is established that the magnitude of the averaged sample coherence estimate is slightly biased for high-resolution coherence maps and that the bias reduces with coarser resolution. Finally, coherence estimation for nonstationary targets is discussed. It is shown that the averaged sample coherence obtained from complex coherence maps or coherence magnitude maps is suitable for estimation of nonstationary coherence. The averaged sample (complex) coherence permits the calculation of an unbiased coherence estimate, provided that the original signals can be assumed to be locally stationary over a sufficiently coarse resolution cell.

Review article: Geometric processing of remote sensing images: models, algorithms and methods

Abstract: The geometric processing of remote sensing images becomes a key issue in multi-source data integration, management and analysis for many geomatic applications. This paper first reviews the source of geometric distortions, compares the different mathematical models being currently used for geometric distortion modelling, details the algorithms, methods and processing steps and finally tracks the error propagation from the input to the final output data.