Abstract:
The development of technology in High spatial resolution has provided opportunities for the application of remote sensing, and the limitation of traditional multi-scale segmentation which is based on region is becoming more obviously. The paper provides a new method of multi-scale segmentation which is combined by edge detection and Regional feature detection. We used nonlinear filtering to segment, and then extracted the information of damaged house caused by earthquake successfully by uniting the edge detection Canny operator embed confidence and the advanced Regional feature detection of multi-scale segmentation using Mean Shift operator. The new method restrains effectively the phenomenon of over segmentation and under segmentation, maintains the space structure of object. The total confident of Kappa reached 0.9623 in the experiment and the time of multi-scale segmentation has shorten less than 20 seconds while processing two images, and the results finally provides the information after disaster.
Keywords:
Edge Detection; Regional Feature Detection; Nonlinear Filtering; Multi-Scale Segmentation; Canny Operator; Mean Shift Operator
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Citations:
APA:
Aquesta, C. (2021). Damaged House Information Extracting of After Disaster Using Multi-Scale Grid Computing Segmentation. International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, 14(2), 47-54. doi: 10.33832/ijgdc.2021.14.2.05.
MLA:
Aquesta, Carmela, et al. “Damaged House Information Extracting of After Disaster Using Multi-Scale Grid Computing Segmentation.” International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, vol. 14, no. 2, 2021, pp. 47-54. IJGDC, http://article.nadiapub.com/IJGDC/vol14_no2/5.html.
IEEE:
[1] C. Aquesta, "Damaged House Information Extracting of After Disaster Using Multi-Scale Grid Computing Segmentation." International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, vol. 14, no. 2, pp. 47-54, July 2021.
