Special Issue

Journal of Real-Time Image Processing

, Volume 1, Issue 2, pp 109-121

First online:

Sensor band selection for multispectral imaging via average normalized information

  • Hongzhi WangAffiliated withDepartment of Computer Science, Stevens Institute of Technology Email author 
  • , Elli AngelopoulouAffiliated withDepartment of Computer Science, Stevens Institute of Technology

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Abstract

The information-rich scene descriptors created by multispectral sensors can act as a bottleneck in further analysis, e.g., real-time scene capturing. Many of the spectral band selection methods treat the two underlying tasks (feature bands selection and redundancy reduction) in isolation. Furthermore, the majority of the work assumes reflectance data. However, the captured surface radiance varies with scene geometry and illumination. We propose a new band selection method, which uses spectral gradient entropy to choose bands that are more stable to such variations. Equally important, our measurement, the average normalized information (ANI) of a set of selected bands, combines feature band selection and band redundancy together. Since feature stability is an important criterion for band selection in ANI, our method favors features whose probability density can be accurately estimated. As a result, our technique selects the most representative feature bands that can be efficiently used in classification. In our experiments, ANI exhibited comparable performance with mutual information on reflectance data but outperformed mutual information when applied on surface radiance data.

Keywords

Multispectral imaging Band selection Entropy Material classification