Onboard payload-data dimensionality reduction

The finer spatial, spectral and radiometric resolutions of current and planned sensors are rendering increasingly-high data rates which, coupled with limited on-board storage, downlink bandwidth and receiving ground station availability, make high-throughput, high-performance data-reduction techniques essential in forthcoming missions. On this paper we describe an algorithm well suited to high-dimensional data as those produced by multispectral and hyperspectral sensors, both highly relevant in a broad range of Earth Observation activities with the latter becoming increasingly available and delivering the highest data rates. The performance of parallel implementations of the algorithm on multi-core and GPU architectures is also evaluated.

[1]  Fabio Del Frate,et al.  Pixel Unmixing in Hyperspectral Data by Means of Neural Networks , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Nicolas H. Younan,et al.  JPEG2000 coding strategies for hyperspectral data , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[3]  Luciano Alparone,et al.  Near-lossless compression of 3-D optical data , 2001, IEEE Trans. Geosci. Remote. Sens..

[4]  Enrico Magli,et al.  Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC , 2004, IEEE Geoscience and Remote Sensing Letters.

[5]  James E. Fowler,et al.  Three‐Dimensional Wavelet‐Based Compression of Hyperspectral Imagery , 2006 .

[6]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[7]  Emmanuel Christophe.  Hyperspectral Data Compression Tradeoff , 2011 .

[8]  Enrico Magli,et al.  Embedded lossy to lossless compression of hyperspectral images using JPEG 2000 , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[9]  Ying Hou.  Lossy-to-Lossless Compression of Hyperspectral Image Using the 3D Set Partitioned Embedded ZeroBlock Coding Algorithm , 2009, J. Softw. Eng. Appl..

[10]  Xin Li,et al.  Progressive Significance Map and Its Application to Error-Resilient Image Transmission , 2012, IEEE Transactions on Image Processing.

[11]  S. Bolton,et al.  Linear Regression and Correlation , 2009 .

[12]  Qian Du,et al.  Improvements to 3D-Tarp Coding for the Compression of Hyperspectral Imagery , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[13]  Chein-I Chang,et al.  Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Guangming Shi,et al.  Hyperspectral image compression using distributed source coding and 3D SPECK , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[15]  William A. Pearlman,et al.  3D set partitioning coding methods in hyperspectral image compression , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[16]  William A. Pearlman,et al.  Hyperspectral image compression using three-dimensional wavelet coding , 2003, IS&T/SPIE Electronic Imaging.

[17]  Paolo Gamba,et al.  Comparison of advanced neural network architectures for hyperspectral data classification , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.