Hyperspectral imaging is an emerging technique in remote sensing data processing that expands and improves capability of multispectral image analysis. It takes advantage of hundreds of contiguous spectral channels to uncover materials that usually cannot be resolved by multispectral sensors. The research conducted in the RSSIPL is focused on spectral techniques, i.e. non-literal techniques that are especially designed and developed for hyperspectral imagery rather than multispectral imagery. Although many techniques already exist in multispectral image processing, some of them may not be effective when they are directly applied to hyperspectral imagery. We take an opposite approach to develop techniques from a hyperspectral imagery viewpoint where noise is generally not Gaussian and interference plays a more dominant role than does noise in hyperspectral image analysis. More importantly, the detection and classification is performed and carried out by targets of interest rather than pattern classes. In particular, we explore applications of statistical signal processing techniques in hyperspectral image analysis, specifically, subpixel detection and mixed pixel classification. Many approaches developed in the RSSIPL over the past years have been documented in a newly published book by Chein-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic/Plenum Publishers. Other techniques developed after this book was published will be documented in a forthcoming book, C.-I Chang, Hyperspectral Imaging: Signal Processing Algorithm Design and Analysis, John Wiley & Sons, due 2006.

Patent Pending

  1. C.-I Chang and Hsuan Ren, Computer-Assisted Target Detection and Classification for Hyperspectral Imagery, June 29 1999.
  2. C.-I Chang and H. Ren, Linearly Constrained Minimum Variance Beamforming Methods for Remote Sensing Image Analysis, May, 2000.
  3. C.-I Chang and D. Heinz, Methods and Apparatus for Constraint Least Squares Linear Spectral Mixture Analysis for Hyperspectral and Multispectral Image Analysis, May 2001.
  4. C.-I Chang and S.-S. Chiang, Method and Apparatus for Anomaly Detection and Classification, February, 2002.
  5. C.-I Chang and J. Wang, FPGA Design for Real-Time Implementaion of Spectral Detection and Classification Algorithms for Remotely Sensed Imagery, May 2003
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Disclosure of Invention

  1. Harsanyi, Joseph and Chang, Chein-I, Orthogonal Subspace Techniques for Image Detection and Classification, May 17, 1993.
  2. Chang, Chein-I and Ren, Hsuan, Automatic Target Detection and Classification in a Blind Environment, May 20, 1997.
  3. Chang, Chein-I and Ren, Hsuan, Linearly Constrained Minimum Variance Beamforming Methods in Remote Sensing Image Analysis, November 10, 1998.
  4. Daniel Heinz and Chein-I Chang, An Efficient Algorithm for Solving Nonnegativey Constrained Optimization Problems, May 28, 1999.
  5. Daniel Heinz and Chein-I Chang, Constraint Least Squares Linear Spectral Mixture Methods for Material Abundance Estimation, Discrimination, Detection, Classification, Identification, Recognition and Quantification, Data Compression and Noise Estimation in Hyperspectral and Multispectral Imagery, February 14, 2000.
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Ph.D. Dissertations

  1. Joseph C. Harsanyi, Detection and Classification of Subpixel Spectral Signatures in Hyperspectral Image Sequences, August 1993.
  2. Brumbley, Kalman Filtering and Subspace Projection Approaches to Multispectral and Hyperspectral Image Classification, May 1998.
  3. Agustine Ifarraguerri, Hyperspectral Image Analysis with Convex Cones and Projection Pursuit, May 2000.
  4. Qian Du, Topics in Hyperspectral Image Analysis, May 2000.
  5. Hsuan Ren, Unsupervised and Generalized Orthogonal Subspace Projection and Constrained Energy Minimization for Target Detection and Classification in Remotely Sensed Imagery, May 2000.
  6. Daniel Heinz, Constrained Least Squares Approaches to Target Detection and Image Classification for Remotely Sensed Images, May 2001.
  7. Shao Shan Chiang, Automatic Target Detection and Classification in Hyperspectral Imagery, May 2001.
  8. Jianwei Wang, Field Programmable Gate Arrays (FPGA) Design for Real-time Implementation of Hyperspectral Detection and Classification Algorithms, May 2003
  9. Kerri Guilfoyle, Application of Linear and Nonlinear Mixture Models to Hyperspectral Imagery Analysis Using Radial Basis Function Neural Networks, May 2003.
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Ph.D. Dissertations in progress

  1. Sumit Chakravarty, VLSI Design for Hyperspectral Image Algorithms
  2. Mingkai Hsueh, Embedded System for Anomaly Detection
  3. Bohong Ji, Constrained Linear Spectral Mixture Analysis
  4. Bharath Ramakrishna, Hyperspectral Image Compression and FPGA Implementations
  5. Gregory Solyar, Nonlinear Mixing Models for Hyperspectral Imagery
  6. Songpo Yang, Unsupervised Classification for Hyperspectral Imagery.
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MASTER THESES

  1. Xiaoli Zhao, Subspace Projection Approaches to Multispectral/Hyperspectral Image Classification Using Linear Spectral Mixture Modeling, May 1996.
  2. Hsuan Ren, A Comparative Study of Mixed Pixel Classification Versus Pure Pixel Classification for Multi/Hyperspectral Imagery, May 1998.
  3. Mingkai Hsueh, Adaptive Causal Anomaly Detection and Its FPGA Implementation, August 2004.
  4. Bharath Ramakrishna, A Comparative Analysis for Hyperspectral Image Compression Techniques, October 2004.
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M.S. SCHOLARLY PAPERS

  1. Jianwei Wang, Image Segmentation Using Multistage Relative Entropy, December 1993.
  2. Qian Du, An Interference and Noise Adjusted Principal Components Analysis, May 1998.
  3. Shao-Shan Chiang, Subpixel Target Detection Using Projection Pursuit, May 1999.
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BOOK CHAPTERS

  1. B. Ramakishna, A. Plaza, C.-I Chang, H. Ren, Q. Du and C.-C. Chang, Spectral/Spatial Hyperspectral Image Compression, Hyperspectral Data Compression, edited by G. Motta and J. Storer, Springer-Verlag, 2005.
  2. C.-I Chang, Least Squares Error Theory for Linear Mixing Problems with Mixed Pixel Classification for Hyperspectral Imagery, Recent Research Developments in Optical Engineering, ed. S.G. Pandalai, vol. 2, 1999, pp. 241-268, Trivandrum, Kerala: Research Signpost, India.
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Journal Publications

    OSP Methods
    1. J. Harsanyi and C.-I Chang, "Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach," IEEE Trans. on Geoscience and Remote Sensing, vol. 32, no. 4, pp. 779-785, July, 1994.
    2. C.-I Chang and H. Ren, "An experiment-based quantitative and comparative analysis of hyperspectral target detection and image classification algorithms," IEEE Trans on Geoscience and Remote Sensing, vol. 38, no. 2, pp. 1044-1063, March 2000.
    3. C.-I Chang, T.-L.E. Sun and M.L.G. Althouse, "Unsupervised interference rejection approach to target detection and classification for hyperspectral imagery," Optical Engineering, vol. 37, pp. 735-743, March 1998.
    4. C.-I Chang, Q. Du, T.S. Sun and M.L.G. Althouse, "A joint band prioritization and band decorrelation approach to band selection for hyperspectral image classification," IEEE Trans. on Geoscience and Remote Sensing, vol. 37, no. 6, pp. 2631-2641, Nov. 1999.
    5. Q. Du, H. Ren and C.-I Chang, ：A comparative study for orthogonal subspace projection andconstrainedenergy minimization： IEEE Trans. on Geoscience and Remote Sensing, vol. 41, no. 6, pp. 1525-1529 June 2003.
    A Posteriori Least Squares OSP Methods
    1. C.-I Chang, T.-L.E. Sun and M.L.G. Althouse, "An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery," Optical Engineering, vol. 37, pp. 735-743, March 1998.
    2. C.-I Chang, X. Zhao, M.L.G. Althouse and J.-J. Pan, "Least squares subspace projection approach to mixed pixel classification in hyperspectral images," IEEE Trans on Geoscience and Remote Sensing, vol. 36, pp. 898-912, May 1998.
    3. C.-I Chang, "Further results on relationship between spectral unmixing and subspace projection," IEEE Trans on Geoscience and Remote Sensing, vol. 36, pp. 1030-1032, May 1998.
    4. C.-I Chang, "Least squares error theory for linear mixing problems with mixed pixel classification for hyperspectral imagery," Recent Research Developments in Optical Engineering, ed. S.G. Pandalai, vol. 2, 1999, pp. 241-268, Trivandrum, Kerala: Research Signpost, India.
    5. C.-I Chang and H. Ren, "An experiment-based quantitative and comparative analysis of hyperspectral target detection and image classification algorithms," IEEE Trans on Geoscience and Remote Sensing, vol. 38, no. 2, pp. 1044-1063, March 2000.
    Unsupervsied OSP Methods
    1. C.-I Chang, T.-L.E. Sun and M.L.G. Althouse, "An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery," Optical Engineering, vol. 37, pp. 735-743, March 1998.
    2. C. Brumbley and C.-I Chang, "An unsupervised vector quantization-based target signature subspace projection approach to classification and detection in unknown background," Pattern Recognition, vol. 32, no. 7, pp. 1161-1174, July 1999.
    3. C.-I Chang and H. Ren, "An experiment-based quantitative and comparative analysis of hyperspectral target detection and image classification algorithms," IEEE Trans on Geoscience and Remote Sensing, vol. 38, no. 2, pp. 1044-1063, March 2000.
    4. H. Ren and C.-I Chang, "A generalized orthogonal subspace projection approach to unsupervised multispectral image classification," IEEE Trans. on Geoscience and Remote Sensing, vol. 38, vol. 6, pp. 2515-2528, September 2000.
    5. H. Ren and C.-I Chang, "Automatic spectral target recognition in hyperspectral imagery," IEEE Trans. on Aerospace and Electronic Systems, vol. 39, no. 4, pp. 1232-1249, October 2003.
    Generalized OSP Methods
    1. H. Ren and C.-I Chang, "A generalized orthogonal subspace projection approach to unsupervised multispectral image classification," IEEE Trans. on Geoscience and Remote Sensing, vol. 38, vol. 6, pp. 2515-2528, September 2000.
    Interference-Annihilated Methods
    1. T.M. Tu, C.-H. Chen and C.-I Chang, "A noise subspace projection approach to target signature detection and extraction in unknown background for hyperspectral images," IEEE Trans. on Geoscience and Remote Sensing, vol. 36, pp. 171-181, 1998.
    2. Q. Du and C.-I Chang, "A signal-decomposed and interference-annihilated approach to hyperspectral target detection," IEEE Trans. on Geoscience and Remote Sensing vol. 42, no. 4, pp. 892-906, April 2004
    Maximum Likelihood Estimation Methods
    1. C.-I Chang, "Further results on relationship between spectral unmixing and subspace projection," IEEE Trans on Geoscience and Remote Sensing, vol. 36, pp. 1030-1032, May 1998.
    2. T.M. Tu, C.-H. Chen, J-L. Wu and C.-I Chang, "A fast two-stage classification method for high dimensional remote sensing data," IEEE Trans. on Geoscience and Remote Sensing, vol. 36, pp. 182-191, 1998.
    3. C.-I Chang and H. Ren, "An experiment-based quantitative and comparative analysis of hyperspectral target detection and image classification algorithms," IEEE Trans on Geoscience and Remote Sensing, vol. 38, no. 2, pp. 1044-1063, March 2000.
    Constrained Least Squares Methods
    1. C.-I Chang and D. Heinz, "Constrained subpixel detection for remotely sensed images," IEEE Trans. on Geoscience and Remote Sensing, vol. 38, vol. 3, 1144-1159, May 2000.
    2. D. Heinz and C.-I Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 39, no. 3, pp. 529-545, March 2001.
    3. C.-I Chang, H. Ren, C.-C. Chang, J.O. Jensen and F. D・Amico, "Estimation of subpixel target size forremotely sensed imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 42, no. 6, pp. 1309-1320, June 2004.
    Eigen-Analysis Methods
    1. C.-I Chang and Q. Du, "Interference and noise adjusted principal components analysis," IEEE Trans. on Geoscience and Remote Sensing, vol. 37, no. 5, pp. 2387-2396, September 1999.
    Linearly Constrained Minimum Variance (LCMV) Methods
    1. C.-I Chang, J.-M. Liu, B.-C. Chieu, C.-M. Wang, C. S. Lo, P.-C. Chung, H. Ren, C.-W. Yang, D.-J. Ma, "A generalized constrained energy minimization approach to subpixel target detection for multispectral imagery," Optical Engineering, vol. 39, no. 5, pp. 1275-1281, May 2000.
    2. H. Ren and C.-I Chang, "Target-constrained interference-minimized approach to subpixel target detection for hyperspectral imagery," Optical Engineering, vol. 39, no. 12, pp. 3138-3145, December 2000.
    3. C.-I Chang, H. Ren and S.S. Chiang, "Real-time processing algorithms for target detection and classification in hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 39, no. 4, pp. 760-768, April 2001
    4. C.-I Chang, "Target signature-constrained mixed pixel classification for hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 40, no. 5, pp. 1065-1081, May 2002.
    5. Q. Du, H. Ren and C.-I Chang, ：A comparative study for orthogonal subspace projection andconstrainedenergy minimization： IEEE Trans. on Geoscience and Remote Sensing, vol. 41, no. 6, pp. 1525-1529 June 2003.
    Linearly Constrained Discriminant Analysis Methods
    1. Q. Du and C.-I Chang, "A linear constrained distance-based discriminant analysis for hyperspectral image classification," Pattern Recognition, vol. 34, no. 2, pp. 361-373, February 2001.
    Kalman Filtering Methods
    1. C.-I Chang and C. Brumbley, "A Kalman filtering approach to multispectral image classification and detection of changes in signature abundance," IEEE Trans. on Geoscience and Remote Sensing, vol. 37, no. 1, pp. 257-268, January 1999.
    2. C.-I Chang and C. Brumbley, "Kalman filtering approach to signature abundance detection, signature estimation and subpixel classification for remotely sensed images," IEEE Trans. Aerospace and Electronics Systems, vol. 37, no 1, pp. 319-330, January 1999.
    3. C. Brumbley and C.-I Chang, "An unsupervised vector quantization-based target signature subspace projection approach to classification and detection in unknown background," Pattern Recognition, vol. 32, no. 7, pp. 1161-1174, July 1999.
    Convex Cone Analysis
    1. A. Ifarragaerri and C.-I Chang, "Multispectral and Hyperspectral image analysis with convex cones," IEEE Trans. on Geoscience and Remote Sensing, vol. 37, no 2, pp. 756-770, March 1999.
    Projection Pursuit
    1. A. Ifarraguerri and C.-I Chang, "Unsupervised hyperspectral image analysis with projection pursuit," IEEE Trans. on Geoscience and Remote Sensing, vol. 38, no. 6, pp. 2529-2538, November 2000.
    2. S.S. Chiang, C.-I Chang and I.W. Ginsberg, "Unsupervised subpixel target detection for hyperspectral images using projection pursuit," IEEE Trans. on Geoscience and Remote Sensing, vol. 39, no. 7, pp. 1380-1391, July 2001
    Anomaly Detection and Classification
    1. C.-I Chang and S.-S. Chiang, "Anomaly detection and classification for hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 40, no. 6, pp. 1314-1325, June 2002.
    Linear Spectral Random Mixture Analysis
    1. C.-I Chang, S.S. Chiang, J.A. Smith and I.W. Ginsberg , "Linear spectral random mixture analysis for hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 40, no. 2, pp. 375-392, February 2002.
    Spectral Characterization
    1. C.-I Chang, "An information-theoretic approach to spectral variability, similarity and discriminability for hyperspectral image analysis," IEEE Trans. on Information Theory, vol. 46, no. 5, pp. 1927-1932, August 2000.
    2. K. Guilfoyle, M.L.G. Althouse and C.-I Chang, "A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks," IEEE Trans. on Geoscience and Remote Sensing, vol. 39, no. 10, pp. 2314-2318, October 2001.
    3. Q. Du and C.-I Chang, "A hidden Markov model approach to spectral analysis for hyperspectral imagery," Optical Engineering, vol. 40, no. 10, pp. 2277-2284, October 2001.
    4. Y. Du, C.-I Chang, H. Ren, C.-C. Chang, J.O. Jensen and F.M. D'Amico, "New hyperspectral discrimination measure for spectral characterization," Optical Engineering, vol. 43, no. 8, pp. 1777-1786, August 2004.
    5. Q. Du and C.-I Chang, "A signal-decomposed and interference-annihilated approach to hyperspectral target detection," IEEE Trans. on Geoscience and Remote Sensing vol. 42, no. 4, pp. 892-906, April 2004
    Data Compression
    1. Q. Du and C.-I Chang, "Linear mixture analysis-based compression for hyperspectral image analysis," IEEE Trans. on Geoscience and Remote Sensing, vol. 42, no. 4, pp. 875-891, April 2004.
    Virtual Dimensionality
    1. C.-I Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 42, no. 3, pp. 608-619, March 2004.
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Conference Publications

    (see Conference Publications in Chein-I Chang)
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Last Update: August 04 2006.
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