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15 October 2015 Advanced signal processing based on support vector regression for lidar applications
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Abstract
The LIDAR technique has recently found many applications in atmospheric physics and remote sensing. One of the main issues, in the deployment of systems based on LIDAR, is the filtering of the backscattered signal to alleviate the problems generated by noise. Improvement in the signal to noise ratio is typically achieved by averaging a quite large number (of the order of hundreds) of successive laser pulses. This approach can be effective but presents significant limitations. First of all, it implies a great stress on the laser source, particularly in the case of systems for automatic monitoring of large areas for long periods. Secondly, this solution can become difficult to implement in applications characterised by rapid variations of the atmosphere, for example in the case of pollutant emissions, or by abrupt changes in the noise. In this contribution, a new method for the software filtering and denoising of LIDAR signals is presented. The technique is based on support vector regression. The proposed new method is insensitive to the statistics of the noise and is therefore fully general and quite robust. The developed numerical tool has been systematically compared with the most powerful techniques available, using both synthetic and experimental data. Its performances have been tested for various statistical distributions of the noise and also for other disturbances of the acquired signal such as outliers. The competitive advantages of the proposed method are fully documented. The potential of the proposed approach to widen the capability of the LIDAR technique, particularly in the detection of widespread smoke, is discussed in detail.
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M. Gelfusa, A. Murari, A. Malizia, M. Lungaroni, E. Peluso, S. Parracino, S. Talebzadeh, J. Vega, and P. Gaudio "Advanced signal processing based on support vector regression for lidar applications", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430E (15 October 2015); https://doi.org/10.1117/12.2194501
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