NDLI: Application of back propagation neural network in the classification of high resolution remote sensing image: Take remote sensing image of beijing for instance

Content Provider	IEEE Xplore Digital Library
Author	Jiefeng Jiang Jing Zhang Gege Yang Dapeng Zhang Lianjun Zhang
Copyright Year	2010
Description	Author affiliation: Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Capital Normal University, Beijing, China (Jiefeng Jiang; Jing Zhang; Gege Yang; Dapeng Zhang; Lianjun Zhang)
Abstract	In recent years, the development of high-resolution remote sensing image extends the visual field of the terrain features. Quickbird and other high-resolution remote sensing image can show more characteristics such as shape, spectral, texture and so on. Back Propagation neural network is widely used in remote sensing image classification in recent years, it is a self-adaptive dynamical system which is widely connected by large amount of neural units, and it bases on distributing store and parallel processing. It study by exercise and had the capacity of integrating the information, synthesis reasoning, and rapid overall processing capacity. It can solve the regular problem arise from remote sensing image processing, therefore, it is widely used in the application of remote sensing. This paper discusses the Back Propagation neural network method in order to improve the high resolution remote sensing image classification precision. By analyzing the principle and learning algorithms of Back Propagation neural network, we utilize the Quickbird imagery of Beijing with high resolution as experimental data and do the research of road and simple building roof, In this paper, the use of remote sensing image processing software Matlab, and then combined with Back Propagation neural network classifier for the high resolution remote sensing images of their pattern recognition.
Starting Page	1
Ending Page	6
File Size	283584
Page Count	6
File Format	PDF
ISBN	9781424473014
e-ISBN	9781424473038
DOI	10.1109/GEOINFORMATICS.2010.5568228
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image resolution Roads Neurons Classification Introduction Artificial neural networks Classification algorithms Back Propagation neural network High-resolution remote sensing image Remote sensing
Content Type	Text
Resource Type	Article

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