[1]WU Shihua,WU Yiquan,ZHOU Jianjiang.Multi-level thresholding for remote sensing image of urban area based on line intercept histogram[J].CAAI Transactions on Intelligent Systems,2018,13(2):227-235.[doi:10.11992/tis.201609012]
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Multi-level thresholding for remote sensing image of urban area based on line intercept histogram
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 2
Page number:
227-235
Column:
学术论文—机器感知与模式识别
Public date:
2018-04-15
- Title:
- Multi-level thresholding for remote sensing image of urban area based on line intercept histogram
- Keywords:
- extraction of urban area; remote sensing image; image segmentation; thresholding; multi-level threshold selection; straight-line intercept histogram; reciprocal grayscale entropy; optimization of artificial bee colony
- CLC:
- TP751.1;P237
- DOI:
- 10.11992/tis.201609012
- Abstract:
- Threshold segmentation is a kind of simple and effective method, however, the existing single-threshold method is hard to realize satisfactory effect in segmenting the images of urban area. In order to segment the remote sensing images of urban area quickly and accurately, a multi-threshold segmentation method based on straight-line intercept histogram, reciprocal grayscale entropy and Artificial Bee Colony (ABC) Optimization was proposed in the paper. Firstly, the straight-line intercept histogram was defined and the straight-line intercept histogram of the urban remote sensing image was established; then the value of the reciprocal grayscale entropy of the histogram was calculated and the single-threshold selection formula was deduced; finally, the application was popularized to multi-threshold selection, ABC Optimization algorithm was utilized for precise optimization of many thresholds, so as to finally realize the multi-threshold segmentation of urban remote sensing images. A large number of experiments show that, the multi-object shape and edge in the images segmented by the method are more accurate, the textures and details are more explicit, in addition, its running time is only 25% of other similar multi-threshold segmentation methods. This is a kind of effective method for segmenting the remote sensing images of urban area.
- References:
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1900-01-01