This paper present a comparative or survey study of object detection or target recognition techniques for synthetic aperture radar images. As know, SAR is an artificial aperture microwave radar or remote sensing equipment which has capability to capture the scene of object by millions of kilometer or where human beings not supposed to reached, generally SAR images known as satellites imaginary. Here in this paper discusses the different techniques and approaches for the target recognition and object detection for SAR imaginary and find the common problem face by the researchers during implementation of such kind of artificial intelligence. Basically ATR i.e. automatic target recognition such as oil spills, missing air-bourn, ships and object identification or recognition at polar surface where human being not supposed to be present or reached has an interested area for the any researchers. In this paper discusses the various techniques such as prescreening method, CFAR, neural networks algorithms, and supervised classifier and many other methods and find the optimal solution or method for fast automatic target recognition and object detection according of their geometry and size. Finally a unique discussion has to be done in this paper and concluded the paper topic.
In this paper discusses the fast target and object detection system or framework for synthetic aperture radar images, as we know these kind of images having high resolution resultant as heavy noise within it. Since the previous implemented algorithms and mechanism haven't suitable for the fast object and target detection for high resolution synthetic aperture radar imaginary because of noise and multispectral. In this paper propose a new hybrid ensemble approach for fast target or object detection for Synthetic aperture radar imaginary. This approach procedure having three steps, firstly remove the noise using transformation method i.e. discrete wavelet transform, second create the appropriate cluster then last using support vector machine (a machine learning approach) for auto segmentation. A comparison has been done with the existing mechanism in term of PSNR, MSE, accuracy and processing time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.