KEYWORDS: Digital signal processing, Field programmable gate arrays, Image processing, Real time imaging, Embedded systems, Imaging systems, Image fusion
The special features of Stereo Imaging for LiDAR and hyperspectral sensor are multi-source data and complex algorithm, which will bring huge challenges to embedded real-time processing. To improve system performance, efficient software design is important. In this paper, based on the hardware platform with FPGA+2C6678, a hierarchical parallel model for software design is studied. In intermediate layer, an adaptive dynamic scheduling strategy and a twostage pipeline parallel architecture based on message transmission are presented, which provide efficient connection between the top application design and the bottom hardware environment. The results indicate that this model is strongly supportive for the high-performance of embedded system, and is beneficial for the open and universal design.
The hyperspectral imageries obtained from dispersive imaging spectrometer often contain significant cross-track spectral curvature nonlinearity disturbances, known as the smile/frown effect, which is due to the change of dispersion angle with field position. The smile effect must be corrected because the across-track wavelength shift from band-center wavelength alters the pixel spectra and reduces the application effect of classification and target recognition. There are several methods to correct the smile effect which don’t take into account the fact that the smile effect is woven together with the sensor radiation characteristic. Individually processing spectra distortion to correct the smile effect would renewably lead to radiometric distortion of the radiometric correction image. A new method is proposed to deal with this problem. An experiment based on the proposed method is conducted. Hyperspectral images are acquired from an UAV airborne Offner Spectral Imager which has a spectral coverage of 0.395~1.028μm. The band of corrected image at 760nm, the absorption peak of O2, has become consistent which shows that the smile effect is effectively removed, and meanwhile the radiometric correction result is finely reserved.
KEYWORDS: Reflectivity, Data modeling, Bidirectional reflectance transmission function, Remote sensing, Solar radiation models, 3D modeling, Scattering, Vegetation, Satellites, Data acquisition
Time series of satellite reflectance data have been widely used to characterize environmental phenomena, describe trends in vegetation dynamics and study climate change. However, several sensors with wide spatial coverage and high observation frequency are usually designed to have large field of view (FOV), which cause variations in the sun-targetsensor geometry in time-series reflectance data. In this study, on the basis of semiempirical kernel-driven BRDF model, a new semi-empirical model was proposed to normalize the sun-target-sensor geometry of remote sensing image. To evaluate the proposed model, bidirectional reflectance under different canopy growth conditions simulated by Discrete Anisotropic Radiative Transfer (DART) model were used. The semi-empirical model was first fitted by using all simulated bidirectional reflectance. Experimental result showed a good fit between the bidirectional reflectance estimated by the proposed model and the simulated value. Then, MODIS time-series reflectance data was normalized to a common sun-target-sensor geometry by the proposed model. The experimental results showed the proposed model yielded good fits between the observed and estimated values. The noise-like fluctuations in time-series reflectance data was also reduced after the sun-target-sensor normalization process.
An Spatially Modulated Fourier Transform Hyperspectral Imager (named HSI) aboard on the Chinese Huan Jing-1A
(HJ-1A) satellite has a spectral coverage of 0.459-0.956μm with 115 spectral bands. In practical, periodical and directional stripe noise was found distributing in the HSI imagery, especially at the first twenty shortwave bands. To
fully utilize all information contained in hyperspectral images, it is demanded to eliminate the stripe noise. This paper
presents a new method to deal with this problem. Firstly, possible sources of HSI stripe noise are analyzed based on interference imaging mechanism. Traditional noises, e.g. device position changes due to launch, non-uniformity in the instrument itself and aging degradations, are directly recorded at the focal plane array and thereafter in the interferogram. After inverse Fourier transform exerted on the interferogram, the spatial dimension of the interference hyperspectral image is restored with complicated and untraceable stripe noises. Therefore traditional image processing methods based on spectral image will not be effective for removing the HSI stripe noise. In order to eliminate this effect, a stripe noise removal method based on interferogram correction is necessary. Then, the implementation process of the interferogram correction method is presented, which mainly contains three steps:
1) Establish relative radiometric correction model of the interferogram based on relatively homogeneous ground scenes as much as possible; 2) Correct the response inconsistency of CCD array by carrying out relative radiometric correction on the interferogram; 3) Convert the corrected interferogram to obtain the revised hyperspectral images. An experiment is conducted and the new method is compared with several traditional methods. The results show that the stripe noise of HSI image can be more effectively removed by the proposed method, and meanwhile the texture detail of original image and the correlation among different bands are finely reserved.
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