Modeling and simulation of optical remote sensing system plays an unslightable role in remote sensing mission
predictions, imaging system design, image quality assessment. It has already become a hot research topic at home and
abroad. Atmospheric turbulence influence on optical systems is attached more and more importance to as technologies of
remote sensing are developed. In order to study the influence of atmospheric turbulence on earth observation system, the
atmospheric structure parameter was calculated by using the weak atmospheric turbulence model; and the relationship of
the atmospheric coherence length and high resolution remote sensing optical system was established; then the influence
of atmospheric turbulence on the coefficient r0h of optical remote sensing system of ground resolution was derived;
finally different orbit height of high resolution optical system imaging quality affected by atmospheric turbulence was
analyzed. Results show that the influence of atmospheric turbulence on the high resolution remote sensing optical system,
the resolution of which has reached sub meter level meter or even the 0.5m, 0.35m and even 0.15m ultra in recent years,
image quality will be quite serious. In the above situation, the influence of the atmospheric turbulence must be corrected.
Simulation algorithms of PSF are presented based on the above results. Experiment and analytical results are posted.
In order to study the influence of atmospheric turbulence on earth observation system, the atmospheric structure parameter was calculated by using the weak atmospheric turbulence model; and the relationship of the atmospheric coherence length and high resolution remote sensing optical system was established; then the influence of atmospheric turbulence on the coefficient r0h of optical remote sensing system of ground resolution was derived; finally different orbit height of high resolution optical system imaging quality affected by atmospheric turbulence was analyzed. Results show that the influence of atmospheric turbulence on the high resolution remote sensing optical system, the resolution of which has reached sub meter level meter or even the 0.5m, 0.35m and even 0.15m ultra in recent years, image quality will be quite serious. In the above situation, the influence of the atmospheric turbulence must be corrected.
An improved method of wavelet threshold denoising is introduced and applied to hyperspectral image denoising. The
method estimates a threshold value for each spectrum. When the signal is transformed to the wavelet domain, a large
number of coefficients with small (or zero) values and a small number of coefficients with large values are gotten.
However, transforming the noise to the wavelet domain produces sort of a scattered distribution of the noise energies
over all scales and translations, assuming that the noise is white. By completing these wavelet transforms, the original
data cube will be completely transformed to the wavelet space. Each component in the transformed cube is considered as
a wavelet coefficient that represents the frequency distribution of the input data, which can reduce the noise in the
spectral domain at the same time. Thresholds are set to a scalar specifying the percentage of cumulative power to retain
in the filtered wavelet transform. Find the actual percent corresponding to these coefficients. During the processing, four
families of mother wavelets (Symlets, Daubechies, Haar and Coiflet) are tested in a series of experiments to estimate the
functioning of those wavelets and threshold parameters. Experimental results show that the proposed algorithm with
Symlets provides an improvement in SNR for hyperspectral data specially.
An improved method of wavelet threshold denoising is introduced and
applied to hyperspectral imagery denoising in spectral domain. This method estimates
a threshold value for each spectrum. Thresholds are set to a scalar specifying the
percentage of cumulative power to retain in the filtered wavelet transform. Find the
actual percent corresponding to these coefficients. During the processing, four
families of mother wavelets (Symlets, Daubechies, Haar and Coiflet) are tested in a
series of experiments to estimate the functioning of those wavelets and thresholding
parameters. Experimental results show that the proposed algorithm with Coiflet
provides an improvement in SNR for hyperspectral data specially.
To meet the requirements of large-scale hyperspectral image analysis and identification applications, a processing flow
of georeferenced mosaic is set up particularly for Hyperion true color images with overlapping areas. The method mainly
includes algorithms of brightness balancing and cutline feathering. Advanced weight-smoothing is applied to blend
image boundaries as a feathering technique, and brightness balancing is fulfilled using Improved-Compensation and
Histogram Matching. In these improved methods gray threshold is setting extra for features of Hyperion data, which
include random high brightness factor (e.g. cloud and mist). Furthermore, all these methods are easily operated and
quite effective for massive data such as Hypeiron images. Finally, a mosaic thematic map (with a scale of 1:100,000) of
8 scenes of Hyperion images is produced based on the research in this paper, which makes the image boundaries natural
and provides a good visual result.
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