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%.

scholarly journals OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

2016 ◽  
Vol XLI-B7 ◽  
pp. 517-522
Author(s):  
Yonghong Jia ◽  
Mingting Zhou ◽  
Ye Jinshan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Multi Scale ◽  
Single Scale ◽  
High Resolution Images ◽  
Image Object ◽  
Scale Classification

The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

scholarly journals OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

2016 ◽  
Vol XLI-B7 ◽  
pp. 517-522
Author(s):  
Yonghong Jia ◽  
Mingting Zhou ◽  
Ye Jinshan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Multi Scale ◽  
Single Scale ◽  
High Resolution Images ◽  
Image Object ◽  
Scale Classification

The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

Remote Sensing Image Classification Based on Object-Oriented Method and Support Vector Machine: A Case Study in Harbin City

2014 ◽  
Vol 912-914 ◽  
pp. 1331-1334
Author(s):  
Qiu Xia Yang ◽  
Chuan Wen Luo ◽  
Tian Kai Chen
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Support Vector ◽  
Object Oriented Method ◽  
Remote Sensing Classification ◽  
Remote Sensing Image Classification ◽  
Harbin City

Remote sensing classification, as an important means of urban planning and construction, has been widely concerned. Urban land use classification is extremely challenging tasks because of some land covers are spectrally too similar to be separated using only the spectral information of remote sensing image. Object-oriented remote sensing image classification method overcomes the drawbacks of traditional pixel-based classification method. It combines the spectral, special structure and texture features of the images, can effectively avoid the phenomenon of "different objects share the same spectrum" or "the same objects differ in spectrum. Support Vector Machine (SVM) is an excellent tool for remote sensing classification. Combination of both can develop their own advantages to do high-resolution remote sensing image classification. Using a public image in Harbin city as an example, classification based on object-oriented method and SVM has achieved better results than traditional pixel-based classification method.

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