Classification of forest species using airborne PHI hyperspectral data

doi:10.6046/gtzyyg.2017.02.16

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Abstract Hyperspectral data are becoming more and more widely used in forestry, especially in terms of classification. Nevertheless, the application of PHI in forestry is much less than that in such fields as agricultural pest and disease monitoring and marine suspended particles monitoring. PHI is used in this paper, and the study area is Jingmen in Hubei Province. This paper proposes an independent component analysis (ICA) combined with adaptive band selection (ABS) algorithm to reduce dimensions, extract forest land and non-forest land using (normalized difference vegetation index,NDVI) based on the subset images, and finally classify the images by support vector machine (SVM), with the overall classification accuracy being 80.70%, and Kappa coefficient reaching 0.75. The results show that the chunk of PHI data and the use of the extraction of NDVI to distinguish between forest land and non-forest land to decrease the effect of “the same object with different spectra” and “the same spectrum with different objects” can yield a good effect. It is shown that the combination of ICA - ABS and SVM is suitable for PHI data. This study has an important significance for the application of hyperspectral in tree species recognition.

Keywords remote sensing image vegetation segmentation spectral histogram multi-scale filtering

Issue Date: 03 May 2017

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	LIU Xiaodan
	YU Ning
	QIU Hongyuan

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LIU Xiaodan,YU Ning,QIU Hongyuan. Classification of forest species using airborne PHI hyperspectral data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 110-116.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.02.16 OR https://www.gtzyyg.com/EN/Y2017/V29/I2/110

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