Share Email Print
cover

Proceedings Paper

Online sparse representation for remote sensing compressed-sensed video sampling
Author(s): Jie Wang; Kun Liu; Sheng-liang Li; Li Zhang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Most recently, an emerging Compressed Sensing (CS) theory has brought a major breakthrough for data acquisition and recovery. It asserts that a signal, which is highly compressible in a known basis, can be reconstructed with high probability through sampling frequency which is well below Nyquist Sampling Frequency. When applying CS to Remote Sensing (RS) Video imaging, it can directly and efficiently acquire compressed image data by randomly projecting original data to obtain linear and non-adaptive measurements. In this paper, with the help of distributed video coding scheme which is a low-complexity technique for resource limited sensors, the frames of a RS video sequence are divided into Key frames (K frames) and Non-Key frames (CS frames). In other words, the input video sequence consists of many groups of pictures (GOPs) and each GOP consists of one K frame followed by several CS frames. Both of them are measured based on block, but at different sampling rates. In this way, the major encoding computation burden will be shifted to the decoder. At the decoder, the Side Information (SI) is generated for the CS frames using traditional Motion-Compensated Interpolation (MCI) technique according to the reconstructed key frames. The over-complete dictionary is trained by dictionary learning methods based on SI. These learning methods include ICA-like, PCA, K-SVD, MOD, etc. Using these dictionaries, the CS frames could be reconstructed according to sparse-land model. In the numerical experiments, the reconstruction performance of ICA algorithm, which is often evaluated by Peak Signal-to-Noise Ratio (PSNR), has been made compared with other online sparse representation algorithms. The simulation results show its advantages in reducing reconstruction time and robustness in reconstruction performance when applying ICA algorithm to remote sensing video reconstruction.

Paper Details

Date Published: 18 November 2014
PDF: 15 pages
Proc. SPIE 9299, International Symposium on Optoelectronic Technology and Application 2014: Optical Remote Sensing Technology and Applications, 92990R (18 November 2014); doi: 10.1117/12.2072148
Show Author Affiliations
Jie Wang, National Univ. of Defense Technology (China)
Kun Liu, National Univ. of Defense Technology (China)
Sheng-liang Li, National Univ. of Defense Technology (China)
Li Zhang, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 9299:
International Symposium on Optoelectronic Technology and Application 2014: Optical Remote Sensing Technology and Applications
Anatoli G. Borovoi; Dong Liu, Editor(s)

© SPIE. Terms of Use
Back to Top