Adaptive inertia weight particle swarm algorithm for optimized hyperspectral image enhancement

Thaweesak Trongtirakul; Sos Agaian

doi:10.1117/12.2585548

12 April 2021 Adaptive inertia weight particle swarm algorithm for optimized hyperspectral image enhancement

Thaweesak Trongtirakul, Sos Agaian

Proceedings Volume 11734, Multimodal Image Exploitation and Learning 2021; 1173403 (2021) https://doi.org/10.1117/12.2585548
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Hyperspectral images deliver several hundred spectral bands covering the visible and the infrared wavelength. Visualize hyperspectral information on a trichromatic display is impracticable to show information, which contains a hundred bands. Therefore, the selection of representative bands and the improvement of image quality are challenging tasks. In this paper, a simple, effective hyperspectral image visualization method based on hyperspectral image enhancement with the use of different cost functions is proposed. The proposed method consists of two major steps. First, the wavelength-based band selection chooses three subsets of adjacent hyperspectral bands. Second, the selected bands are improved by the proposed fractional contrast stretching algorithm with optimizations. In the nature-inspired optimization algorithm with an adaptive inertia weight, the quality of an enhanced image will be evaluated by different cost functions – image quality measures. This paper's main contribution is that: i) the selection of representative bands corresponding to natural-color appearance, ii) the image enhancement for visualization, and iii) the investigation of the suitable cost function for hyperspectral image visualization. Experiments performed on several hyperspectral datasets illustrate that the proposed method can produce remarkable visualization performance in subjective and objective assessments.

Citation Download Citation

Thaweesak Trongtirakul and Sos Agaian "Adaptive inertia weight particle swarm algorithm for optimized hyperspectral image enhancement", Proc. SPIE 11734, Multimodal Image Exploitation and Learning 2021, 1173403 (12 April 2021); https://doi.org/10.1117/12.2585548

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