UAV Downwash-Based Terrain Classification Using Wiener-Khinchin and EMD Filters

Matos-Carvalho, J. P.; Mora, André; Rato, Raul Tello; Mendonca, Ricardo; Fonseca, José

doi:10.1007/978-3-030-17771-3_7

Cited by 2 publications

(6 citation statements)

References 17 publications

(22 reference statements)

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“…In [10,12,16], there are images taken by satellites or at a distance above 60 meters from the ground, which increases the error in the classification of the studied terrain types. In [8,18], the UAV was also used at a short distance from the ground, as in this article, also providing images in high resolution. However, ref.…”

Section: Comparison and Discussion Of Resultsmentioning

confidence: 99%

“…However, ref. [8,18] only classify one terrain which clearly becomes a disadvantage when there may be several terrain types to be captured by the on-board camera. This work's developed system has the ability to classify different terrain types in the same image.…”

Section: Comparison and Discussion Of Resultsmentioning

confidence: 99%

“…This action clearly shows an improved system accuracy, since each terrain behaves differently when affected by the UAV's downwash effect. According to the works cited in Table 3, only [18] used the dynamic terrain feature, observing that the proposed system's accuracy is greater than in [8,10,12,16] articles and enhancing the terrains dynamism when it comes to terrain classification. Compared to [18], this work implements different static and dynamic algorithms, as well as classifies different terrain types with increased accuracy.…”

Section: Comparison and Discussion Of Resultsmentioning

confidence: 99%

“…The final result will emphasize the color that most represents the terrain type (eg, blue for water). Additionally, frequency domain [7,8], segmentation [6,9,10], bayesian network [11], and Hyperspectal Images [12] can also be used in terrain classification.Other types of sensors such as LiDAR [13][14][15][16] can complement the classification decision. Algorithms that use laser scanners proved to be qualified to accurately distinguish between water and non-water terrains [13][14][15][16].…”

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Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

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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.Several methods for terrain classification have been proposed in different works. Texture algorithms, such as those proposed in [2][3][4][5], have been widely recommended to emphasize the high and low frequencies of the images, supporting image classification. Other algorithms use color information to classify terrains, such as presented in [6], which is able to distinguish four different terrain types within an image. During this process, each channel's pixel is divided by the square root of its own three channels intensity. The final result will emphasize the color that most represents the terrain type (eg, blue for water). Additionally, frequency domain [7,8], segmentation [6,9,10], bayesian network [11], and Hyperspectal Images [12] can also be used in terrain classification.Other types of sensors such as LiDAR [13][14][15][16] can complement the classification decision. Algorithms that use laser scanners proved to be qualified to accurately distinguish between water and non-water terrains [13][14][15][16]. However, shallow water terrains increase the decision error due to laser reflection, which leads to a misclassification as non-water terrain.Although prior research work has proposed many good solutions for terrain classification, there is still a gap regarding the study of dynamic terrain. The previously mentioned algorithms suffer from a high sensitivity to changes in the environment, mainly due to changes in brightness, color and texture. Recent works [17,18] ...

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Section: Comparison and Discussion Of Resultsmentioning

confidence: 99%

Section: Comparison and Discussion Of Resultsmentioning

confidence: 99%