PCA-based classification using airborne hyperspectral radiance data, a case study: Mount Horshan Mediterranean forest

Abstract

Atmospheric correction (ATC) of radiance image data is a preliminary and necessary procedure to reach a coherent unsupervised classification. Though ATC results in removal of noise artefacts related to path radiance, loss of some data is inherent by the process. The unsupervised principal component analysis-based classification (PCABC) was harnessed in this paper using radiance data that bypass the ATC protocol. Being primarily based on the variability of the input hyperspectral remote sensing (HRS) image regardless of its physical attributes, it was assumed that PCABC can be applied to radiance HRS image just as already shown on reflectance domain. To test this assumption, PCABC was tested on a radiance HRS image of Specim’s AisaFENIX taken over the Mediterranean forest of Mount Horshan, Israel. With no application of ATC or noise reduction, while tested unsupervised classification methods were insufficient, PCABC was able to classify four different plant species with an overall accuracy of 68%.

Keywords:

• Hyperspectral remote sensing
• radiance image
• unsupervised classification
• plant species classification

Acknowledgments

The authors would like to thank the forest rangers of KKL for their support during the conductance of this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.