Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule
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 geometr...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2015-06-01
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Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W4/75/2015/isprsarchives-XL-7-W4-75-2015.pdf |
Summary: | 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%. |
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ISSN: | 1682-1750 2194-9034 |