Use of Particle Swarm Optimization in Terrain Classification based on UAV Downwash

Kim, Iuliia; Matos-Carvalho, J. P.; Viksnin, Ilya I.; Campos, Luis Miguel; Fonseca, José; Mora, André; Chuprov, Sergey

doi:10.1109/cec.2019.8790031

Cited by 2 publications

(2 citation statements)

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“…In this section, it is presented the first static texture algorithm-Gray-Level Co-Occurrence Matrix, known as GLCM [36,37]. This algorithm extracts statistical texture features, that represents textures based on the relation and the distribution between pixels of a given frame.…”

Section: Gray-level Co-occurrence Matrixmentioning

confidence: 99%

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

et al. 2019

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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: Gray-level Co-occurrence Matrixmentioning

confidence: 99%

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

et al. 2019

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“…This paper is an extension of the work [5]. It contains an extended formalization of the developed computer vision algorithm.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Embedded in UAV as a Method of Territory-Monitoring Efficiency Improvement

et al. 2022

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Unmanned aerial vehicles have large prospects for organizing territory monitoring. To integrate them into this sphere, it is necessary to improve their high functionality and safety. Computer vision is one of the vital monitoring aspects. In this paper, we developed and validated a methodology for terrain classification. The overall classification procedure consists of the following steps: (1) pre-processing, (2) feature extraction, and (3) classification. For the pre-processing stage, a clustering method based on particle swarm optimization was elaborated, which helps to extract object patterns from the image. Feature extraction is conducted via Gray-Level Co-Occurrence Matrix calculation, and the output of the matrix is turned into the input for a feed-forward neural network classification stage. The developed computer vision system showed 88.7% accuracy on the selected test set. These results can provide high quality territory monitoring; prospectively, we plan to establish a self-positioning system based on computer vision.

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Use of Particle Swarm Optimization in Terrain Classification based on UAV Downwash

Cited by 2 publications

References 12 publications

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

Particle Swarm Optimization Embedded in UAV as a Method of Territory-Monitoring Efficiency Improvement

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