Object-oriented classification of high-resolution remote sensing image using structural feature

2010 ◽  
Author(s):  
Lei Li ◽  
Ning Shu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Structural Feature ◽  
Object Oriented ◽  
Remote Sensing Image

scholarly journals Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule

2015 ◽  
Vol XL-7/W4 ◽  
pp. 75-80 ◽  
Author(s):  
C. K. Li ◽  
W. Fang ◽  
X. J. Dong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Image Data ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Multi Level ◽  
Image Object

With the development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing data is greatly improved. How to efficiently process and interpret the massive high resolution remote sensing image data for ground objects, which with spatial geometry and texture information, has become the focus and difficulty in the field of remote sensing research. An object oriented and rule of the classification method of remote sensing data has presents in this paper. Through the discovery and mining the rich knowledge of spectrum and spatial characteristics of high-resolution remote sensing image, establish a multi-level network image object segmentation and classification structure of remote sensing image to achieve accurate and fast ground targets classification and accuracy assessment. Based on worldview-2 image data in the Zangnan area as a study object, using the object-oriented image classification method and rules to verify the experiment which is combination of the mean variance method, the maximum area method and the accuracy comparison to analysis, selected three kinds of optimal segmentation scale and established a multi-level image object network hierarchy for image classification experiments. The results show that the objectoriented rules classification method to classify the high resolution images, enabling the high resolution image classification results similar to the visual interpretation of the results and has higher classification accuracy. The overall accuracy and Kappa coefficient of the object-oriented rules classification method were 97.38%, 0.9673; compared with object-oriented SVM method, respectively higher than 6.23%, 0.078; compared with object-oriented KNN method, respectively more than 7.96%, 0.0996. The extraction precision and user accuracy of the building compared with object-oriented SVM method, respectively higher than 18.39%, 3.98%, respectively better than the object-oriented KNN method 21.27%, 14.97%.

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
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Texture Feature ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Maximum Likelihood Classification ◽  
Information Classification ◽  
Remote Sensing Image Classification ◽  
Other Information

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.

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