Determination of target detection limits in hyperspectral data using band selection and dimensionality reduction

W. Gross, J. Boehler, K. Twizer, B. Kedem, A. Lenz, M. Kneubuehler, P. Wellig, R. Oechslin, H. Schilling, S. Rotman, W. Middelmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Hyperspectral remote sensing data can be used for civil and military applications to robustly detect and classify target objects. High spectral resolution of hyperspectral data can compensate for the comparatively low spatial resolution, which allows for detection and classification of small targets, even below image resolution. Hyperspectral data sets are prone to considerable spectral redundancy, affecting and limiting data processing and algorithm performance. As a consequence, data reduction strategies become increasingly important, especially in view of near-real-time data analysis. The goal of this paper is to analyze different strategies for hyperspectral band selection algorithms and their effect on subpixel classification for different target and background materials. Airborne hyperspectral data is used in combination with linear target simulation procedures to create a representative amount of target-to-background ratios for evaluation of detection limits. Data from two different airborne hyperspectral sensors, AISA Eagle and Hawk, are used to evaluate transferability of band selection when using different sensors. The same target objects were recorded to compare the calculated detection limits. To determine subpixel classification results, pure pixels from the target materials are extracted and used to simulate mixed pixels with selected background materials. Target signatures are linearly combined with different background materials in varying ratios. The commonly used classification algorithms Adaptive Coherence Estimator (ACE) is used to compare the detection limit for the original data with several band selection and data reduction strategies. The evaluation of the classification results is done by assuming a fixed false alarm ratio and calculating the mean target-to-background ratio of correctly detected pixels. The results allow drawing conclusions about specific band combinations for certain target and background combinations. Additionally, generally useful wavelength ranges are determined and the optimal amount of principal components is analyzed.

Original languageAmerican English
Title of host publicationTarget and Background Signatures II
EditorsRic H. M. A. Schleijpen, Karin U. Stein
ISBN (Electronic)9781510603981
StatePublished - 1 Jan 2016
EventTarget and Background Signatures II - Edinburgh, United Kingdom
Duration: 26 Sep 201627 Sep 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering


ConferenceTarget and Background Signatures II
Country/TerritoryUnited Kingdom


  • Band selection
  • Detection limit
  • Dimensionality reduction
  • Hyperspectral
  • Simulation
  • Subpixel
  • Target detection
  • Target-to-background ratio

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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