2019
DOI: 10.3390/rs11212501
|View full text |Cite
|
Sign up to set email alerts
|

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

Abstract: 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 t… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
2
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
3
3
1
1

Relationship

1
7

Authors

Journals

citations
Cited by 18 publications
(3 citation statements)
references
References 40 publications
0
2
0
Order By: Relevance
“…Terrain classification is important for an emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation systems. Using three algorithms (Gray-Level Co-Occurrence Matrix, Gray-Level Run Length Matrix, and Flow), the research [36] provided a complete solution for terrain classification in differentiating among the four terrain types (water, vegetation, asphalt, and sand). Their proposed solution developed on the FPGA achieved a 95.14% success rate in train classification using the OpenCV library.…”
Section: Object Detection Tracking and Environment Monitoringmentioning
confidence: 99%
“…Terrain classification is important for an emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation systems. Using three algorithms (Gray-Level Co-Occurrence Matrix, Gray-Level Run Length Matrix, and Flow), the research [36] provided a complete solution for terrain classification in differentiating among the four terrain types (water, vegetation, asphalt, and sand). Their proposed solution developed on the FPGA achieved a 95.14% success rate in train classification using the OpenCV library.…”
Section: Object Detection Tracking and Environment Monitoringmentioning
confidence: 99%
“…The latest technological developments in processing power and volume of data facilitate the adoption of learning approaches. These methods try to find patterns in the extracted characteristics of the fabric image and therefore can be used standalone or as a complement to other methods like LBP [26] and GLCM [27]. SVMs [28], Feed-forward Networks (FFNs) [29], [30] and CNNs [31]- [33] are some of the methods included in this type of approaches.…”
Section: Learning Approachesmentioning
confidence: 99%
“…The continuous development of aerial photography technology makes it possible for people to collect numerous high-resolution remote sensing images, which contributes a lot to many important applications in the remote sensing field [1][2][3]. Some typical applications include classification [4,5], image segmentation [6] and object detection [7][8][9][10]. Specifically, object detection, which tries to precisely estimate the class and locations of objects contained in each image, is one primary task [11][12][13].…”
Section: Introductionmentioning
confidence: 99%