气候变化领域集成服务门户(CCPortal): Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images

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DOI	10.1016/j.rse.2020.111938
	Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images
	Zhang B.; Zhao L.; Zhang X.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Airborne hyperspectral remote sensing data with both rich spectral and spatial features can effectively improve the classification accuracy of vegetation species. However, the spectral data of hundreds of bands brings about problems such as dimensional explosion, which poses a huge challenge for hyperspectral remote sensing classification based on classical parameters models. Deep learning methods have been used for remotely sensed images classification in recent years, but the popular HSI datasets including Kennedy Space Center, Indian Pines, Pavia University scene and Salinas scene, have low spatial resolution, significant differences between categories, and regular boundaries. When applied to the classification of forestry tree species, the accuracy often decreases because the spectral response of different plants of the same family and genus are very similar, especially under the fragmented species distribution, complex topography and the occluded canopy. So we collect new data sets, selected Gaofeng State Owned Forest Farm in Guangxi province in south China as the research area and adopted the airborne hyperspectral data obtained by the LiCHy system of the Chinese Academy of Forestry to explore an improved three-dimensional convolutional neural network(3D-CNN) model for tree species classification. The proposed model uses raw data as input without dimension reduction or feature screening, and simultaneously extracts spectral and spatial features. After the 3D convolutional layer, the captured high-level semantic concept is a joint spatial spectral feature representation, so we can turn it into a one-dimensional feature as a new input to learn a more abstract level of expression. The widely used earlystop method is also used to prevent overfitting. The proposed model is a lightweight, generalized, and fast convergence classification model, by which the short-time and large-area of multiple tree species classification with high-precision can be realized. The result shows that the 3D-1D CNN model can shorten the training time of the 3D CNN model by 60% and achieve a classification accuracy of 93.14% within 50 ha in 6.37 min, which provides a basis for the classification of tree species, the mapping of forest form and the inventory of forest resources. © 2020 Elsevier Inc.
英文关键词	3D-1D-CNN; 3D-CNN; Airborne hyperspectral image; Classification; Tree species
语种	英语
scopus关键词	3D modeling; Classification (of information); Convolution; Convolutional neural networks; Deep learning; Forestry; Learning systems; Lithium compounds; Remote sensing; Semantics; Space optics; Space platforms; Spectroscopy; Timber; Topography; Airborne hyperspectral data; Airborne hyperspectral remote sensing; Classification accuracy; Classification models; Hyperspectral remote sensing; Kennedy space centers; One-dimensional features; Remotely sensed images; Image classification; airborne sensing; artificial neural network; image classification; learning; parameterization; precision; remote sensing; satellite data; spatial resolution; topography; training; vegetation mapping; China; Guangxi Zhuangzu
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179228
作者单位	The Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, 100083, China; Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing, 100083, China; Institute of Information Engineering, Chinese Academy of Sciences, No. 89, Minzhuang Road, Haidian District, Beijing, 100093, China
推荐引用方式 GB/T 7714	Zhang B.,Zhao L.,Zhang X.. Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images[J],2020,247.
APA	Zhang B.,Zhao L.,&Zhang X..(2020).Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images.Remote Sensing of Environment,247.
MLA	Zhang B.,et al."Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images".Remote Sensing of Environment 247(2020).