NDLI: The Research on Image Classification of Remote Sensing Based on an Improved Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Mu Bai Huiping Liu Wenli Huang Xiaoluo Zhou Xiaodong Mu
Copyright Year	2008
Description	Author affiliation: Sch. of Geogr., Beijing Normal Univ., Beijing (Mu Bai; Huiping Liu; Wenli Huang; Xiaoluo Zhou; Xiaodong Mu)
Abstract	With higher spatial resolution, the image classification of remote sensing is always a hot research field. Besides spectral information, texture information from remote sensing image of higher spatial resolution has become an important data source to improve the classification accuracy. The image classification approach adopts an improved neural network, which contains two steps connected by the refusal principle. Two steps of input neurons are spectral information, using 3times3 window size, and texture information from gray co-occurrence matrix, which is selected by the genetic algorithms. The final result which is to overlay of above results get higher accuracy that the traditional method that ANN combine simply all of information from different source as input neurons.
File Size	391711
File Format	PDF
ISBN	9781424428076
DOI	10.1109/IGARSS.2008.4779176
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image classification Remote sensing Neural networks Neurons Spatial resolution Remote monitoring Genetic algorithms Artificial neural networks Pixel Image analysis Accuracy ANN Texture Spectrum
Content Type	Text
Resource Type	Article

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