Object Oriented Information Classification of Remote Sensing Image Based on Segmentation and Merging

2014 ◽  
Vol 568-570 ◽  
pp. 734-739
Author(s):  
Xiao Li Liu

The spectral characteristic to classify the remote sensing image classification methods based on pixels of tradition, and the object oriented classification method besides the spectral information, texture feature, also includes the spatial structure of images and other information, so the classification accuracy is very high. In this paper, the remote sensing image based on object oriented classification, puts forward the classification of remote sensing image segmentation based on multiple information combination. Experiments show that, this method can overcome the pixel maximum likelihood classification based on frequent pepper phenomenon of tradition, greatly improves the classification accuracy and reliability. and has better visual effect.

2012 ◽  
Vol 546-547 ◽  
pp. 508-513 ◽  
Author(s):  
Qiong Wu ◽  
Ling Wei Wang ◽  
Jia Wu

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.


2012 ◽  
Vol 127 ◽  
pp. 237-246 ◽  
Author(s):  
Alexis Comber ◽  
Peter Fisher ◽  
Chris Brunsdon ◽  
Abdulhakim Khmag

2013 ◽  
Vol 405-408 ◽  
pp. 3001-3006 ◽  
Author(s):  
Shuang Ting Wang ◽  
Chun Lai Wang ◽  
Wei Bing Du ◽  
Le Le Tong ◽  
Fei Wang

Pepper and Salt" phenomenon and misclassification phenomenon are serious and the accuracy is low based on pixel classification, when only use a single remote sensing image. In this article, joint LiDAR data and high resolution image together based on feature per-parcel classification,and in the image segmentation stage, texture feature is introduced, these can full use of spectral informationtexture feature and elevation information in classification, to solve same object with different spectra and same spectrum with different objects. Compared with the classification based on pixel, only use a single remote sensing image, the method based on feature per-parcel with spectrumtexture and elevation information achieved a high accuracy,96.94%, improved the classification result.


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