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Open AccessArticle

Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation

1
Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2
Nanjing Research Institute of Electronics Engineering, Nanjing 210007, China
3
University of Calgary, Calgary, AB T2P 2M5, Canada
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 993; https://doi.org/10.3390/s20040993
Received: 11 January 2020 / Revised: 5 February 2020 / Accepted: 10 February 2020 / Published: 12 February 2020
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Unmanned aerial vehicles (UAV) have had significant progress in the last decade, which is applied to many relevant fields because of the progress of aerial image processing and the convenience to explore areas that men cannot reach. Still, as the basis of further applications such as object tracking and terrain classification, semantic image segmentation is one of the most difficult challenges in the field of computer vision. In this paper, we propose a method for urban UAV images semantic segmentation, which utilizes the geographical information of the region of interest in the form of a digital surface model (DSM). We introduce an Affiliated Fusion Conditional Random Field (AF-CRF), which combines the information of visual pictures and DSM, and a multi-scale strategy with attention to improve the segmenting results. The experiments show that the proposed structure performs better than state-of-the-art networks in multiple metrics. View Full-Text
Keywords: semantic image segmentation; CRF; DSM; UAV; remote sensing semantic image segmentation; CRF; DSM; UAV; remote sensing
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MDPI and ACS Style

Kong, Y.; Zhang, B.; Yan, B.; Liu, Y.; Leung, H.; Peng, X. Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation. Sensors 2020, 20, 993.

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