Methods of UAVs images segmentation based on k-means and a genetic algorithm

Abstract

The object of this study is the process of segmentation of images from unmanned aerial vehicles. It was established that segmentation methods based on k-means and a genetic algorithm work qualitatively on images from space observation systems. It is proposed to use segmentation methods based on k-means and a genetic algorithm for segmenting images from unmanned aerial vehicles. The main stages of image segmentation methods based on k-means and genetic algorithm have been determined. An experimental study of segmentation of images from unmanned aerial vehicles was carried out. Unlike known ones, image segmentation by a k-means-based method that successfully works on images from space surveillance systems cannot be directly applied to image segmentation from unmanned aerial vehicles. Unlike known ones, image segmentation by a method based on a genetic algorithm that successfully works on images from space surveillance systems also cannot be directly applied to image segmentation from unmanned aerial vehicles. The quality of segmentation of images from unmanned aerial vehicles by methods based on k-means and a genetic algorithm was assessed. It was established that: – the average level of first-kind errors is 70 % and 51 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively; – average level of second-kind errors is 61 % and 43 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively. It was concluded that further research must be carried out to develop methods for segmenting images from unmanned aerial vehicles.

Devising a method for segmenting images acquired from space optical and electronic observation systems based on the Sine-Cosine algorithm

“…Formalization of the problem of segmentation of images acquired from space optoelectronic surveillance systems based on the Sine-Cosine algorithm can be written with expression (1) [25,26,28]:…”

“…Segmentation of the original image from the space optoelectronic surveillance system f(x, y) involves the partitioning of f(x, y) into segments B i according to condition (2) [25,26,28]:…”

“…where B: B = {B 1 , B 2 ,..., B K } -parsing segments in the segmented image fs(x, y); K is the number of partition segments, LP is the predicate, (i = 1, 2, ..., K). The LP predicate is calculated using expression (3) [25,26,28]:…”

“…Paper [26] proposes methods for segmenting images of remote sensing of the Earth based on the k-means algorithm and genetic algorithm. It was experimentally established that segmentation methods based on the k-means algorithm and based on the genetic algorithm have good segmentation results only for satellite images.…”

“…For images acquired from UAVs, these algorithms cannot be applied directly. The advantage of the methods [26] is the reduction of segmentation errors of the first and second kind for space images in comparison with known methods. The main disadvantage of the methods in [26] is their limitations in the processing of all types of remote sensing data.…”