CURRENT TRENDS IN AEROSPACE IMAGE PROCESSING AND PATTERN RECOGNITION
DOI:
https://doi.org/10.34185/1991-7848.itmm.2023.01.096Keywords:
aerospace images, linear filtering, independent component analysis, pattern matching, generative adversarial networks.Abstract
This paper aims to analyze current trends in the processing and recognition of aerospace images. Such images are an important source of information for various industries, such as military and civilian cartography, agriculture, and ecology. The process of processing and analyzing large-scale aerospace images requires significant time and resources, so there is a need to use modern machine learning and image processing methods. This paper describes various methods for processing aerospace images, such as linear filtering, independent component analysis, pattern matching, and generative adversarial networks. The use of modern methods for processing and pattern recognition of aerospace images is an essential step in improving the efficiency and accuracy of large-scale image analysis, which can be useful for various industries that use aerospace images.
References
Gomes, V.C.F.; Queiroz, G.R.; Ferreira, K.R. An Overview of Platforms for Big Earth Observation Data Management and Analysis. RemoteSens. 2020, 12, 1253. https://doi.org/10.3390/rs12081253
Bovik, Alan C., and Scott T. Acton. "Basic linear filtering with application to image enhancement." The Essential Guide to Image Processing. Academic Press, 2009. 225-239.
Naik, Ganesh R., and Dinesh K. Kumar. "An overview of independent component analysis and its applications." Informatica 35.1 (2011).
Lewis, John P. "Fast template matching." Vision interface. Vol. 95. No. 120123. 1995.
Aggarwal, Alankrita, Mamta Mittal, and GopiBattineni. "Generative adversarial network: An overview of theory and applications." International Journal of Information Management Data Insights 1.1 (2021): 100004.
