ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 111-117, 2016
https://doi.org/10.5194/isprs-annals-III-7-111-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jun 2016
A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION
Ping Wang1, Zheng Wei1, Weihong Cui2, and Zhiyong Lin2 1South China Sea Institute of Planning and Environment Research, SOA, Guangzhou, China
2School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Keywords: Statistical Learning, Minimum Spanning Tree (MST), Image Segmentation Rule Abstract. This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.
Conference paper (PDF, 962 KB)


Citation: Wang, P., Wei, Z., Cui, W., and Lin, Z.: A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 111-117, https://doi.org/10.5194/isprs-annals-III-7-111-2016, 2016.

BibTeX EndNote Reference Manager XML