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Recent Advances in Real-Time

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Title: Recent Advances in Real-Time


1
  • Recent Advances in Real-Time
  • Hyperspectral Image Processing
  • Mingkai Hsueh
  • Remote Sensing Signal and Image Processing
    Laboratory
  • Department of Computer Science and Electrical
    Engineering
  • University of Maryland Baltimore County
  • 1000 Hilltop Circle, Baltimore, MD 21250

2
Outline
  • Introduction to Hyperspectral Image Processing
    and its Applications
  • Anomaly Detection
  • Anomaly Detection
  • Real-time implementation
  • Speed-up of Adaptive Causal Anomaly Detection
  • Conclusions
  • Projects

3
Hyperspectral Image
Mixed pixel (soil mineral)
Water
Mixed pixel (trees soil)
4
Applications of Hyperspectral Image Processing
  • Applications
  • Man-made objects canvas, camouflage,
    military vehicles in defense applications
  • Toxic waste, oil spills in environmental
    monitoring
  • Landmines
  • Trafficking in law enforcement
  • Chemical/biological agent detection
  • Special species in agriculture, ecology

5
Types of Signatures
  • Endmembers
  • Pure signatures for a spectral class used for
    spectral unmixing
  • Anomalies
  • Signals/signatures spectrally distinct from
  • their surroundings, i.e., abnormality.
  • rare minerals in geology
  • abnormal activities in military applications.

6
RX Algorithm
  • RX algorithm basically performs the Mahalanobis
    distance that is specified by
  • (ri-?)T (K)-1 (ri -?)
  • The required mean vector µ hinder the possibility
    of implementing the algorithm in real-time
    fashion.

7
Causal RX Filter (CRXF)
  • By replacing the covariance matrix by correlation
    matrix, we can achieve the real-time processing.
  • The functional form of CRXF
  • riT (Ri)-1 ri
  • The major drawback is that if a detected anomaly
    remains on the image to be processed, it may
    decrease the detectability of the following
    anomalies.

8
Adaptive Causal Anomaly Detector (ACAD)
  • ACAD has the same functional form as does CRXF,
    except the sample correlation matrix R is formed
    by all the arrived pixel vectors except the
    detected anomalous target pixel vectors that have
    been removed.
  • riT (Ri)-1 ri
  • An anomalous target map is generated at the same
    time as the detection process takes place.

9
HYDICE Data
  • HYDICE (Hyperspectral Digital Imagery Collection
    Experiment)
  • 15 panels of five types with three different
    materials.
  • They are arranged into a matrix in such a way
    that each row represents 3 panels of the same
    type with three different sizes, 3m?3m, 2m?2m,
    1m?1m. Each column represents 5 panels of
    different types with the same size.

Anomaly
Original image
Target masked image
10
CRXF Results
row 8
row 16
row 24
row 32
row 40
row 48
row 56
row 64
11
ACAD Results
row 8
row 16
row 24
row 32
row 40
row 48
row 56
row 64
12
ACAD Target Map
row 8
row 16
row 24
row 32
row 64
row 40
row 48
row 56
13
ACAD Hardware Design
Ri Ri-1 ri riT
Auto Correlator
(Ri)-1 (Qi Riupper )-1 ( Riupper )-1 QiT
QR Matrix Inverse
Abundance Calculation
dACAD (ri) riT (RiT)-1 ri
Anomalous Target Discriminator
tK t
14
Matrix Inversion Lemma
(ABCD)-1 A-1 A-1B(C-1DA-1B)-1 DA-1
By Woodburys identity, set B a column vector, C
a scalar of unity, and D a row vector
?
(ArrT)-1 A-1 (A-1rrT A-1) / (1rTA-1r)
  • Let A be the current correlation matrix and r be
    the incoming pixel vector.

15
Matrix Inversion Lemma (Contd)
  • With Matrix Inversion Lemma (MIL), we only need
    to compute
  • Using MIL the matrix inversion is reduced to
    matrix multiplications.
  • Simulation is provided to evaluate the
    performance of MIL.

16
ACAD Hardware Design
Ri Ri-1 ri riT
Auto Correlator
(Ri)-1 (Qi Riupper )-1 ( Riupper )-1 QiT
QR Matrix Inverse
Abundance Calculation
dACAD (ri) riT (RiT)-1 ri
Anomalous Target Discriminator
tK t

Matrix Inversion Lemma
17
Speed-up of MIL
  • We use two versions of the MATLAB program to
    perform the ACAD on the same image cube. One uses
    the MATLAB inv() function and another one uses
    the MIL.

With MIL Without MIL
Computation time 26.6090 45.6560
  • As we can see, the speed-up is about 2 times
    faster for the 64x64 HYDICE image than the one
    without MIL.

18
Conclusions
  • The Matrix Inversion Lemma has been successfully
    applied to reduce the matrix inversion performed
    by Adaptive Causal Anomaly Detection (ACAD) into
    matrix multiplications.
  • Since the Causal RX Filter (CRXF) and Real-time
    CEM (Constrained Energy Minimization) previously
    proposed in Wang 2003 also involve inverse
    matrix computation, the same MIL-based approach
    can be also applied to reduce the computational
    load.

19
Future Work
  • An effective Dimensionality Reduction (DR) or
    Band Selection (BS) may need to reduce the number
    of bands to an acceptable range so that we can
    further reduce the computation cost in both
    applications.
  • Heterogeneous platform may be also considered to
    reduce the design time and possibly achieve
    better performance.

20
Projects Conducted in RSSIPL
  • Joint Service Agent Water Monitor
  • Mission
  • Develop GUI image analysis software for detecting
    Biological Threat Agent on Handheld Assays
  • Ported developed algorithms onto embedded system,
    Stargate Gateway (SPB400, Linux single board
    computer) with external hand held scanner device.
  • Sponsor
  • US Army Edgewood Chemical and Biological Center
    (ECBC)
  • ANP Technologies, Inc.

21
Projects Conducted in RSSIPL (Contd)
22
Projects Conducted in RSSIPL (Contd)
  • Multi-band Multi-threat warning sensor
  • Mission
  • Developed detection algorithms for missile and
    grenade images captured from real-time
    Multispectral imaging system.
  • Developed MATLAB based GUI for image analysis.
  • Sponsor
  • Surface Optics Corporation (SOC)

23
Software for Detecting Agents
24
Embedded System
25
Projects Conducted in RSSIPL (Contd)
  • Multi-band Multi-threat warning sensor
  • Mission
  • Developed detection algorithms for missile and
    grenade images captured from real-time
    Multispectral imaging system.
  • Developed MATLAB based GUI for image analysis.
  • Sponsor
  • Surface Optics Corporation (SOC)

26
Publication
  • Book Chapter
  • J. Wang, M. Hsueh and C.-I Chang, FPGA Design
    for Second-order Statistics Based Target
    Detection Algorithm for Hyperspectral Imagery
    Applications, High Performance Computing in
    Remote Sensing, Chapman Hall/CR, Oct 2007.
  • J. Wang, M. Hsueh and C.-I Chang, FPGA
    Implementation for Real-time Orthogonal Subspace
    Projection for Hyperspectral Imagery
    Applications, High Performance Computing in
    Remote Sensing, Chapman Hall/CR, Oct 2007.

27
Publication (contd)
  • Journal
  • C.-I Chang and M. Hsueh, Characterization of
    Anomaly Detection in Hyperspectral Imagery,
    Sensor Review, Volume 26, Issue 2, pp. 137-146,
    2006.
  • M. Hsueh and C.-I Chang, Field Programmable Gate
    Arrays for Pixel Purity Index Using Blocks of
    Skewers for Endmember Extraction in Hyperspectral
    Imagery, International Journal of High
    Performance Computing Applications, Dec 2007. (to
    appear)
  • C.-I Chang, M. Hsueh, F. Chaudhry, W. Liu, C.-C.
    Wu, G. Solyar, A pyramid-based block of skewers
    for pixel purity index for endmember Extraction
    in hyperspectral imagery, International Journal
    of High Speed Electronics and Systems. (to
    appear)
  • M. Hsueh and C.-I Chang, Adaptive Causal Anomaly
    Detection on Reconfigurable Computing, IEEE
    Transaction on Industrial Electronics. (To be
    submitted)

28
Publication (contd)
  • Conference
  • M. Hsueh and C.-I Chang, FPGA implementation of
    Adaptive Causal Anomaly Detection, 2006 CIE
    Annual Convention, Newark, NJ, Sep 16, 2006.
  • C.-I Chang, M. Hsueh, F. Chaudhry, W. Liu, C.
    C. Wu, A. Plaza and G. Solyar, A Pyramid-based
    Block of Skewers for Pixel Purity Index for
    Endmember Extraction in Hyperspectral Imagery,
    2006 International Symposium on Spectral Sensing
    Research, Bar Harbor, ME, May 29 to Jun 2, 2006.
  • D. Valencia, A. Plaza, M. A. Vega-Rodriguez, R.
    M. Perez and M. Hsueh, FPGA Design and
    Implementation of a Fast Pixel Purity Index
    Algorithm for Endmember Extraction in
    Hyperspectral Imagery, SPIE Optics East, Boston,
    MA, Oct 23-26 2005.
  • L. Wu, J. Wang, B. Ramakrishna, M. Hsueh, J. Liu,
    Q. Wu, C. Wu, M. Cao, C. Chang, J. L. Jensen, J.
    O. Jensen, H. Knapp, R. Daniel, R. Yin, An
    embedded system developed for hand held assay
    used in water monitoring, SPIE Optics East,
    Boston, MA, Oct 23-26, 2005.

29
Publication (contd)
  • Conference
  • M. Hsueh and C.-I Chang, Adaptive Causal Anomaly
    Detection for Hyperspectral Imagery, IEEE
    International Geoscience and Remote Sensing
    Symposium, Alaska, Sep 19-26, 2004.
  • M. Hseuh, A. Plaza, J. Wang, S. Wang, W. Liu,
    C.-I Chang, J. L. Jensen and J. O. Jensen,
    Morphological algorithms for processing tickets
    by hand held assay, OpticsEast, Chemical and
    Biological Standoff Detection II (OE120), Vol.
    5584, Philadelphia, PA, Oct 25-28, 2004.
  • C.-I Chang, H. Ren, M. Hsueh, F. DAmico and J.O.
    Jensen, A Revisit to Target-Constrained
    Interference-Minimized Filter, 48th Annual
    Meeting, SPIE International Symposium on Optical
    science and Technology, Imaging Spectrometry IX (
    AM110), San Diego, CA, Aug 3-8, 2003.
  • S. T. Sheu, M. Hsueh, An Intelligent Cell
    Checking Policy for Promoting Data Transfer
    Performance in Wireless ATM Networks, IEEE ATM
    Workshop '99, Kochi City, Kochi, Japan, May
    24-27, 1999.

30
Thank you!!
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