干旱区生态环境知识资源中心(Arid): Hyperspectral vegetation identification utilizing polynomial fitting for dimensionality reduction

Arid

DOI	10.1117/12.2558172
	Hyperspectral vegetation identification utilizing polynomial fitting for dimensionality reduction
	van der Laan, John D.; Redman, Brian J.; Anderson, Dylan Z.; West, R. Derek; Yocky, David
通讯作者	van der Laan, JD (corresponding author), Sandia Natl Labs, 1515 Eubank Blvd SE, Albuquerque, NM 87123 USA.
会议名称	Conference on Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXI held at SPIE Defense + Commercial Sensing Conference
会议日期	APR 27-MAY 08, 2020
会议地点	ELECTR NETWORK
英文摘要	Identification of vegetation species and type is important in many chemical, biological, radiological, nuclear, and explosive sensing applications. For instance, emergence of non-climax species in an area may be indicative of anthropogenic activity which can complement prompt signatures for underground nuclear explosion detection and localization. To explore signatures of underground nuclear explosions, we collected high spatial resolution (10 cm) hyperspectral data from an unmanned aerial system at a legacy underground nuclear explosion test site and its surrounds. These data consist of 274 visible and near-infrared wavebands over 4.3 km(2) of high desert terrain along with high spatial resolution (2.5 cm) RGB context imagery. Previous work has shown that a vegetation spectral derivative can be more indicative of species than the measured value of each band. However, applying a spectral derivative amplifies any noise in the spectrum and reduces the benefit of the derivative analysis. Fitting the spectra with a polynomial can provide the slope information (derivative) without amplifying noise. In this work, we simultaneously capture slope and curvature information and reduce the dimensionality of remotely sensed hyperspectral imaging data. This is performed by employing a 2nd order polynomial fit across spectral bands of interest. We then compare the classification accuracy of a support vector machine classifier fit to the polynomial dimensionality reduction technique and the same support vector machine fit to the same number of components from principle component analysis.
英文关键词	hyperspectral imagery underground nuclear explosions unmanned aerial systems vegetation classification
来源出版物	CHEMICAL, BIOLOGICAL, RADIOLOGICAL, NUCLEAR, AND EXPLOSIVES (CBRNE) SENSING XXI
ISSN	0277-786X
EISSN	1996-756X
出版年	2020
卷号	11416
ISBN	978-1-5106-3610-1
出版者	SPIE-INT SOC OPTICAL ENGINEERING
类型	Proceedings Paper
语种	英语
收录类别	CPCI-S
WOS记录号	WOS:000590002900022
WOS关键词	CLASSIFICATION ; INDEXES ; UAV
WOS类目	Chemistry, Applied ; Optics ; Spectroscopy
WOS研究方向	Chemistry ; Optics ; Spectroscopy
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/337093
作者单位	[van der Laan, John D.; Redman, Brian J.; Anderson, Dylan Z.; West, R. Derek; Yocky, David] Sandia Natl Labs, 1515 Eubank Blvd SE, Albuquerque, NM 87123 USA
推荐引用方式 GB/T 7714	van der Laan, John D.,Redman, Brian J.,Anderson, Dylan Z.,et al. Hyperspectral vegetation identification utilizing polynomial fitting for dimensionality reduction[C]:SPIE-INT SOC OPTICAL ENGINEERING,2020.