NDLI: Quantitative retrieval of sparse vegetation coverage by BP neural network based on hyperspectral data

Content Provider	IEEE Xplore Digital Library
Author	Xiaosong Li Zhihai Gao
Copyright Year	2010
Description	Author affiliation: Institute of Remote Sensing Applications, Chinese academy of science, Beijing, China (Xiaosong Li) \|\| Research Institute of Forest Resource Information Techniques, Chinese academy of forestry, Beijing, China (Zhihai Gao)
Abstract	With parallel processing, nonlinear mapping, adaptive learning and fault-tolerant capacities, neural net has always been the ideal tool for establishing the complex relationship between complex spectral information and target parameters. We constructed a two-layer BP neural net model with three input nodes, two hidden layer nodes and one output node. Based on which, sparse vegetation coverage in Minqin oasis-desert transitional zone, Gansu province, was estimated through selecting independent principal component as input. The results show that the accuracy of retrieval based on BP neural net was high, validation RMSE was only 3.2806, about 16% in the average. Therefore, BP neural network model is an effective means to retrieve sparse vegetation coverage accurately n arid regions, based on hyperspectral data.
Starting Page	1557
Ending Page	1560
File Size	593015
Page Count	4
File Format	PDF
ISBN	9781424465132
e-ISBN	9781424465163
DOI	10.1109/CISP.2010.5646960
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Biological system modeling Vegetation mapping BP neural net Artificial neural networks Vegetation hyperspectral sparse vegetation coverage principal component Hyperspectral imaging
Content Type	Text
Resource Type	Article

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