Parallel real-time virtual dimensionality estimation for hyperspectral images

One of the most important tasks in hyperspectral imaging is the estimation of the number of endmembers in a scene, where the endmembers are the most spectrally pure components. The high dimensionality of hyperspectral data makes this calculation computationally expensive. In this paper, we present several new real-time implementations of the well-known Harsanyi–Farrand–Chang method for virtual dimensionality estimation. The proposed solutions exploit multi-core processors and graphic processing units for achieving real-time performance of this algorithm, together with better performance than other works in the literature. Our experimental results are obtained using both synthetic and real images. The obtained processing times show that the proposed implementations outperform other hardware-based solutions.

[1]  Chein-I Chang,et al.  Constrained band selection for hyperspectral imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Antonio J. Plaza,et al.  A Hybrid CPU–GPU Real-Time Hyperspectral Unmixing Chain , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[4]  Antonio J. Plaza,et al.  Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs , 2012, Journal of Real-Time Image Processing.

[5]  Antonio J. Plaza,et al.  Real-Time Implementation of the Vertex Component Analysis Algorithm on GPUs , 2013, IEEE Geoscience and Remote Sensing Letters.

[6]  Antonio J. Plaza,et al.  The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends , 2013, Proceedings of the IEEE.

[7]  Chein-I. Chang Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .

[8]  Ashok Patel,et al.  Optimal noise benefits in Neyman-Pearson signal detection , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Michael Frankfurter,et al.  Numerical Recipes In C The Art Of Scientific Computing , 2016 .

[10]  Sebastián López,et al.  A novel FPGA-based architecture for the estimation of the virtual dimensionality in remotely sensed hyperspectral images , 2014, Journal of Real-Time Image Processing.

[11]  Xin Wu,et al.  GPU-Based Parallel Design of the Hyperspectral Signal Subspace Identification by Minimum Error (HySime) , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Antonio J. Plaza,et al.  Parallel implementation of endmember extraction algorithms from hyperspectral data , 2006, IEEE Geoscience and Remote Sensing Letters.

[13]  José M. Bioucas-Dias,et al.  Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[14]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[15]  Antonio J. Plaza,et al.  Real-Time Identification of Hyperspectral Subspaces , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Antonio J. Plaza,et al.  Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs , 2012, Journal of Real-Time Image Processing.

[17]  L. Scharf,et al.  Statistical Signal Processing: Detection, Estimation, and Time Series Analysis , 1991 .

[18]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .