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: LIDAR, Clouds, Digital signal processing, Sensors, Image processing, Data conversion, Data modeling, Data fusion, Imaging systems, Real time imaging
The real-time processing based on embedded system will enhance the application capability of stereo imaging for LiDAR and hyperspectral sensor. The task partitioning and scheduling strategies for embedded multiprocessor system starts relatively late, compared with that for PC computer. In this paper, aimed at embedded multi-core processing platform, a parallel model for stereo imaging is studied and verified. After analyzing the computing amount, throughout capacity and buffering requirements, a two-stage pipeline parallel model based on message transmission is established. This model can be applied to fast stereo imaging for airborne sensors with various characteristics. To demonstrate the feasibility and effectiveness of the parallel model, a parallel software was designed using test flight data, based on the 8-core DSP processor TMS320C6678. The results indicate that the design performed well in workload distribution and had a speed-up ratio up to 6.4.
Currently, the design of embedded signal processing system is often based on a specific application, but this idea is not conducive to the rapid development of signal processing technology. In this paper, a parallel processing model architecture based on multi-core DSP platform is designed, and it is mainly suitable for the complex algorithms which are composed of different modules. This model combines the ideas of multi-level pipeline parallelism and message passing, and summarizes the advantages of the mainstream model of multi-core DSP (the Master-Slave model and the Data Flow model), so that it has better performance. This paper uses three-dimensional image generation algorithm to validate the efficiency of the proposed model by comparing with the effectiveness of the Master-Slave and the Data Flow model.
Hyperspectral Light Detection And Ranging (Hyperspectral LiDAR), a recently developed technique, combines the advantages of the LiDAR and hyperspectral imaging and has been attractive for many applications. Supercontinuum laser (SC laser), a rapidly developing technique offers hyperspectral LiDAR a suitable broadband laser source and makes hyperspectral Lidar become an installation from a theory. In this paper, the recent research and progressing of the hyperspectral LiDAR are reviewed. The hyperspectral LiDAR has been researched in theory, prototype system, instrument, and application experiment. However, the pulse energy of the SC laser is low so that the range of the hyperspectral LiDAR is limited. Moreover, considering the characteristics of sensors and A/D converter, in order to obtain the full waveform of the echo, the repetition rate and the pulse width of the SC laser needs to be limited. Recently, improving the detection ability of hyperspectral LiDAR, especially improving the detection range, is a main research area. A higher energy pulse SC laser, a more sensitive sensor, or some algorithms are applied in hyperspectral LiDAR to improve the detection distance from 12 m to 1.5 km. At present, a lot of research has been focused on this novel technology which would be applied in more applications.
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