Research on classification of hyperspectral remote sensing image based on improved NPA in SVM

2008 ◽  
Author(s):  
Zhaoqing Shen ◽  
Ning Shu ◽  
Jianbin Tao ◽  
Jie Sun ◽  
Zulong Lai
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Remote Sensing Image

Ensemble Learning on Hyperspectral Remote Sensing Image Classification

2012 ◽  
Vol 546-547 ◽  
pp. 508-513 ◽  
Author(s):  
Qiong Wu ◽  
Ling Wei Wang ◽  
Jia Wu
Keyword(s):  
Remote Sensing ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Remote Sensing Image Classification ◽  
Hyperspectral Remote Sensing Image

The characteristics of hyperspectral data with large number of bands, each bands have correlation, which has required a very high demand of solving the problem. In this paper, we take the features of hyperspectral remote sensing data and classification algorithms as the background, applying the ensemble learning to image classification.The experiment based on Weka. I compared the classification accuracy of Bagging, Boosting and Stacking on the base classifiers J48 and BP. The results show that ensemble learning on hyperspectral data can achieve higher classification accuracy. So that it provide a new method for the classification of hyperspectral remote sensing image.

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