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Open AccessArticle

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

1
Beyond Vision, 3830-352 Ílhavo, Portugal
2
NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
3
Centre of Technology and Systems, UNINOVA, 2829-516 Caparica, Portugal
4
School of Technology and Management, Polytechnic Institute of Beja, 7800-295 Beja, Portugal
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PDMFC, 1300-609 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2501; https://doi.org/10.3390/rs11212501
Received: 26 September 2019 / Revised: 21 October 2019 / Accepted: 23 October 2019 / Published: 25 October 2019
(This article belongs to the Section Remote Sensing Image Processing)
The ability to precisely classify different types of terrain is extremely important for Unmanned Aerial Vehicles (UAVs). There are multiple situations in which terrain classification is fundamental for achieving a UAV’s mission success, such as emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation. Previous research works describe different terrain classification approaches mainly using static features from RGB images taken onboard UAVs. In these works, the terrain is classified from each image taken as a whole, not divided into blocks; this approach has an obvious drawback when applied to images with multiple terrain types. This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV’s downwash effect: Static textures- Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic textures- Optical Flow method. This system has been fully implemented using the OpenCV library, and the GLCM algorithm has also been partially specified in a Hardware Description Language (VHDL) and implemented in a Field Programmable Gate Array (FPGA)-based platform. In addition to these feature extraction algorithms, a neural network was designed with the aim of classifying the terrain into one of four classes. Lastly, in order to store and access all the classified terrain information, a dynamic map, with this information was generated. The system was validated using videos acquired onboard a UAV with an RGB camera. View Full-Text
Keywords: image processing; texture; GLCM; GLRLM; optical flow; terrain classification; UAV; downwash effect; FPGA image processing; texture; GLCM; GLRLM; optical flow; terrain classification; UAV; downwash effect; FPGA
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Matos-Carvalho, J.P.; Moutinho, F.; Salvado, A.B.; Carrasqueira, T.; Campos-Rebelo, R.; Pedro, D.; Campos, L.M.; Fonseca, J.M.; Mora, A. Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery. Remote Sens. 2019, 11, 2501.

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