Classification study of Mikania micrantha kunth from UAV hyperspectral image band selection

doi:10.13474/j.cnki.11-2246.2020.0108

Abstract

Abstract: Mikania micrantha kunth is one of the most harmful invasive species, which has caused serious damage to our country’s forest ecosystem for its’ rapidly growing and spread.The relevant management needs an effective method for monitoring Mikania micrantha kunth.The traditional methods of manual investigation requires a lot of manpower and material resources,which are considerably costly and inefficient.In recent years,the rapid development of hyperspectral remotes sensing technology provids a new method of monitoring the Mikania micrantha kunth.This paper bases on the remote sensing image data of Zengcheng forest farm in Guangdong Province,which is obtained by the Nano-Hyperspec hyperspectral imaging instrument carried by UAV. The hyperspectral data is pretreatmented by geometric correction,image denoising,radiometric calibration and bad band elimination. OIF, ABS, ASP+ABS methods for band selection are used, obtaining the characteristic wave band of the most informative and low correlation band to constitute the optimal band combination for Mikania micrantha kunth. Generating three remote sensing images, and using the support vector machine method (SVM) classify the three different remote sensing images.The degree of response of the three band combinations to the hyperspectral characteristics of Mikania micrantha kunth is evaluated with the accuracy of the classification results, and a band combination better reflected the spectral characteristics of Mikania micrantha kunth is selected. The experimental results show the cartographic accuracy and user accuracy are 74.62% and 66.52% by using the method of OIF, 74.37% and 67.43% by using the method of ABS, 95.98% and 92.98% by using the method of ASP+ABS,which has the best classification accuracy. Compared with the method of OIF, the method of ASP+ABS improvs by 21.35% and 26.46%.Compared with the method of ABS, the ASP+ABS improves by 17.15% and 19.3%.Compared with the other two methods, the band selection methods of ASP+ABS in this paper has the better performance of reflecting spectral characteristics of Mikania micrantha kunth, which can provide an effective technical method of monitoring the Mikania micrantha kunth.

Key words: Mikania micrantha kunth, hyperspectral image, band selection, optimal index factor, adaptive band selection, auto-subspace partition and adaptive band selection, classification

CLC Number:

P237

LIU Yanjun, ZHANG Gui, WANG Xiao, ZHOU Cui, YANG Zhigao, WU Xin, ZHANG Juan. Classification study of Mikania micrantha kunth from UAV hyperspectral image band selection[J]. Bulletin of Surveying and Mapping, 2020, 0(4): 34-39,52.

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