Abstract
One of the most effective ways to improve accuracy and speed of recognition algorithms is to preliminary distinguish the regions of interest in the analyzed images. We studied a possibility of application of self-organizing maps and a Kohonen neural network for detection of regions of interest at a radar or satellite image of underlying surface. There is a high probability of finding an object of interest for further analysis in the found regions of interest. The definition of region of interest is necessary most of all to automate and speed up the process of search and recognition of objects of interest. The relevance is due to the increasing number of satellites. The study presents the process of modeling, analysis and comparison of the results of application of these methods for determination of regions of interest in recognition of images of aircraft against the background of underlying surface. It also describes the process of preliminary processing of input data. The study presents a general approach to construction and training of the Kohonen self-organizing map and neural network. Application of Kohonen maps and neural network makes it possible to decrease an amount of data analyzed by 15–100 times. It speeds up the process of detection and recognition of an object of interest. Application of the above algorithm reduces significantly the required number of training images for a convolutional network, which performs the final recognition. The reduction of a training sample occurs because the size of parts of an input image supplied to the convolutional network is bounded with the scale of an image and it is equal to the size of the largest detected object. Kohonen neural network showed itself more efficient in relation to this task, since it places cluster centers on the underlying surface rarely due to independence of weight of neurons on neighboring centers. These technical solutions could be used in the analysis of visual data from satellites, aircraft, and unmanned cars, in medicine, robotics, etc.
Highlights
At present, the low speed of image recognition is one of the main problems hampering the development of modern visual data processing systems
The most modern type of neural networks (NN) used in image recognition is the convolutional neural network (CNN), which got its name because of the presence of a convolution operation [1]
To reduce the time required for an analysis and necessary computational resources in detection of objects of interest at an input image, we propose to select zones of their possible location with further sequential analysis of found zones in the convolutional NN
Summary
The low speed of image recognition is one of the main problems hampering the development of modern visual data processing systems. The algorithm presented in this paper allows us to reduce the number of data analyzed by convolutional NN when detecting objects in an image by 15–20 times and, reduce the search time for necessary objects, increasing recognition accuracy and saving computational resources. This is achieved by using the network and or Kohonen map to determine regions of interest (ROIs) on the radar or satellite image of the underlying surface on the input image, in which the probability of detection of the desired object is high. This paper discusses the application of the developed algorithm for detecting and recognizing the type of aircraft (LA) on satellite images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
