Application and Evaluation of Object-Oriented Technology in High-Resolution Remote Sensing Image Classification

2011 ◽  
Author(s):  
Chuan Zhang ◽  
Yingjun Zhao ◽  
Donghui Zhang ◽  
Ningbo Zhao
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Object Oriented Technology ◽  
Remote Sensing Image Classification

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%.

High Resolution Remote Sensing Image Classification Combining with Mean-Shift Segmentation and Fully Convolution Neural Network

2018 ◽  
Vol 55 (2) ◽  
pp. 022802
Author(s):  
方旭 Fang Xu ◽  
王光辉 Wang Guanghui ◽  
杨化超 Yang Huachao ◽  
刘慧杰 Liu Huijie ◽  
闫立波 Yan Libo
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Mean Shift ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Remote Sensing Image Classification
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