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Author Li, Dawei ♦ Yang, Fengbao ♦ Wang, Xiaoxia
Source SpringerLink
Content type Text
Publisher Springer India
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Natural sciences & mathematics ♦ Earth sciences
Subject Keyword Remote sensing ♦ Classification ♦ Crop information extraction ♦ Adaboost ♦ Support vector machine ♦ BPNN ♦ Earth Sciences ♦ Remote Sensing/Photogrammetry
Abstract High resolution remote sensing image contains abundant information, but remote sensing classification only based on spectral information is affected in the complex spectrum area. Crop area and other land-cover objects contain different texture features. This paper extracts texture information based on gray-level co-occurrence matrix and Gabor filters group, sets up spectrum-texture joint feature set. To enhance classification efficiency, Ensemble learning strategy is introduced to improve classical support vector machine and back propagation neural network classifiers in training process. To prove the effectiveness of proposed methods, several experiment images are utilized to execute experiments. Results indicate that proposed methods improve classification accuracy compared with classical algorithms significantly, and promote running efficiency compared with the situation of large sample, support corn area statistical process and yield estimation.
ISSN 0255660X
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-06-30
Publisher Place New Delhi
e-ISSN 09743006
Journal Journal of the Indian Society of Remote Sensing
Volume Number 45
Issue Number 2
Page Count 9
Starting Page 229
Ending Page 237


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Source: SpringerLink