scholarly journals A Stabilization of MC-BCS-SPL Scheme for Distributed Compressed Video Sensing

2017 ◽  
Vol 20 (5) ◽  
pp. 731-739
Author(s):  
Joong-seon Ryu ◽  
Jin-soo Kim
Keyword(s):  
Compressed Video ◽  
Distributed Compressed Video Sensing

scholarly journals Distributed Compressed Video Sensing in Camera Sensor Networks

2012 ◽  
Vol 8 (12) ◽  
pp. 352167 ◽  
Author(s):  
Yu Liu ◽  
Xuqi Zhu ◽  
Lin Zhang ◽  
Sung Ho Cho
Keyword(s):  
Sensor Networks ◽  
Compressed Video ◽  
Distributed Compressed Video Sensing ◽  
Camera Sensor

scholarly journals Distributed Compressed Hyperspectral Sensing Imaging Based on Spectral Unmixing

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2305 ◽  
Author(s):  
Zhongliang Wang ◽  
Hua Xiao
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Interpolation Method ◽  
Side Information ◽  
Hyperspectral Imagery ◽  
Original Data ◽  
Distributed Source Coding ◽  
Spectral Unmixing ◽  
Compressed Video ◽  
Distributed Compressed Video Sensing

The huge volume of hyperspectral imagery demands enormous computational resources, storage memory, and bandwidth between the sensor and the ground stations. Compressed sensing theory has great potential to reduce the enormous cost of hyperspectral imagery by only collecting a few compressed measurements on the onboard imaging system. Inspired by distributed source coding, in this paper, a distributed compressed sensing framework of hyperspectral imagery is proposed. Similar to distributed compressed video sensing, spatial-spectral hyperspectral imagery is separated into key-band and compressed-sensing-band with different sampling rates during collecting data of proposed framework. However, unlike distributed compressed video sensing using side information for reconstruction, the widely used spectral unmixing method is employed for the recovery of hyperspectral imagery. First, endmembers are extracted from the compressed-sensing-band. Then, the endmembers of the key-band are predicted by interpolation method and abundance estimation is achieved by exploiting sparse penalty. Finally, the original hyperspectral imagery is recovered by linear mixing model. Extensive experimental results on multiple real hyperspectral datasets demonstrate that the proposed method can effectively recover the original data. The reconstruction peak signal-to-noise ratio of the proposed framework surpasses other state-of-the-art methods.

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