The Target Detection Wizard guides you through the process to find targets in hyperspectral or multispectral images. The targets may be a material or mineral of interest (such as Alunite) or they may be man-made objects (such as military vehicles).

To start the wizard, select Target Detection > Target Detection Wizard from the Toolbox.

The Wizard guides you through the following target detection steps:

  1. Select Input/Output Files: Select the input image and the root name for output images.
  2. Perform Atmospheric Correction: Optionally convert the image into reflectance through atmospheric correction.
  3. Select Target Spectra: Select one or more target spectra for the analysis from spectral libraries, individual spectral plots, text files, Regions Of Interest (ROIs), or statistics files.
  4. Select Non-Target Spectra: Optionally select one or more spectra to suppress from processing.
  5. Apply MNF Transform: Optionally apply the Minimum Noise Fraction (MNF) transform before target detection analysis.
  6. Select Target Detection Methods: Choose up to eight target detection methods for processing.
  7. Load Rule Images and Preview: Load the output rule images and preview the binary results.
  8. Filter Targets: Select from different filter options and parameters for each target to clean up mis-detected pixels and false positives.
  9. Export Results: Export results to one shapefile and/or ROI per target detection method.
  10. View Statistics and Report: View target detection statistics for each selected method and a report of settings used in the Wizard.


X. Jin, S. Paswaters, and H. Cline, "A comparative study of target detection algorithms for hyperspectral imagery," In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. Proceedings of SPIE, Vol. 7334, pp. 73341W1-73341W12.

C.-I Chang, J.-M. Liu, B.-C. Chieu, C.-M. Wang, C. S. Lo, P.-C. Chung, H. Ren, C.‑W. Yang, and 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. (CEM)

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. (TCIMF)

S. Johnson, “Constrained energy minimization and the target-constrained interference-minimized filter,” Optical Engineering, vol. 42, no. 6, pp. 1850-1854, June 2003. (CEM and TCIMF)

S. Kraut, L. L. Scharf, and R. W. Butler, “The adaptive coherence estimator: a uniformly most-powerful-invariant adaptive detection statistic,” IEEE Trans. on Signal Processing, vol. 53, no. 2, pp. 427-438, 2005. (ACE)

D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Laboratory Journal, vol. 14, pp. 79-116, 2003. (ACE)

J. C. 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, 1994. (OSP)

C.-I Chang, “Further results on relationship between spectral unmixing and subspace projection,” IEEE Trans. on Geosciences and Remote Sensing, vol. 36, pp. 1030-1032, May 1998. (OSP)

C.-I Chang, “Hyperspectral Imaging: Techniques for Spectral Detection and Classification,” Kluwer Academic Publishers, Dordrecht. 2003. (OSP, CEM, TCIMF)

J. W. Boardman, “Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering,” In: 7th JPL Airborne Geoscience Workshop, pp. 55-56, 1998. (MTMF)

Related Topics

THOR Target Detection