ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 9-12, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W4-9-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Nov 2017
MULTIPLE AND SINGLE GREEN AREA MEASUREMENTS AND CLASSIFICATION USING PHANTOM IMAGES IN COMPARISON WITH DERIVED EXPERIMENTAL LAW
N. A. M. Abu-Zaid Electrical and Communication Engineering Departments, An-najah National University, Nablus, Palestinian territories
Keywords: Adaptive weighted distances algorithm, Object classification, Curve fitting, Arial images Abstract. In many circumstances, it is difficult for humans to reach some areas, due to its topography, personal safety, or security regulations in the country. Governments and persons need to calculate those areas and classify the green parts for reclamation to benefit from it.To solve this problem, this research proposes to use a phantom air plane to capture a digital image for the targeted area, then use a segmentation algorithm to separate the green space and calculate it's area. It was necessary to deal with two problems. The first is the variable elevation at which an image was taken, which leads to a change in the physical area of each pixel. To overcome this problem a fourth degree polynomial was fit to some experimental data. The second problem was the existence of different unconnected pieces of green areas in a single image, but we might be interested only in one of them. To solve this problem, the probability of classifying the targeted area as green was increased, while the probability of other untargeted sections was decreased by the inclusion of parts of it as non-green. A practical law was also devised to measure the target area in the digital image for comparison purposes with practical measurements and the polynomial fit.
Conference paper (PDF, 968 KB)


Citation: Abu-Zaid, N. A. M.: MULTIPLE AND SINGLE GREEN AREA MEASUREMENTS AND CLASSIFICATION USING PHANTOM IMAGES IN COMPARISON WITH DERIVED EXPERIMENTAL LAW, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 9-12, https://doi.org/10.5194/isprs-annals-IV-4-W4-9-2017, 2017.

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