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Convergence analysis of the CNN algorithm in target recognition using SAR images
Author(s): Ligang Zou; Zhijun Qiao
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Paper Abstract

With the rapid development of artificial intelligence technology and the emergence of a large number of innovative theories, the concept of deep learning is widely used in object detection, speech recognition, language translation and other fields. One of the important practices is target recognition in SAR images. Although it shows certain effectiveness in some researches, when using deep learning algorithm, there are still many problems that have not yet been solved. For example, the convergence of algorithms has not shown intuitively, although the high precision of experimental results can be obtained. There are many reasons that lead to the results divergence, such as the size of the database, the type of model, and the algorithm used in the experiment. This paper aims at analyzing the factors that influence the convergence of the results from the perspective of the CNN algorithm. The goal can be achieved by means of constraint of convergence condition. Firstly, by controlling the amount of data in the database, the influence of the size of the database will be determined. Secondly, the radius of convergence will be analyzed, based on which, the scope of application will be found. Combining the above two factors, a corresponding method can be given in the final paper, which gives rise to the convergence of the result. Finally, the correctness of the above theories will be explained by conducting experiments using the MSTAR database.

Paper Details

Date Published: 3 May 2019
PDF: 6 pages
Proc. SPIE 11003, Radar Sensor Technology XXIII, 110030W (3 May 2019); doi: 10.1117/12.2519086
Show Author Affiliations
Ligang Zou, The Univ. of Texas Rio Grande Valley (United States)
Zhijun Qiao, The Univ. of Texas Rio Grande Valley (United States)


Published in SPIE Proceedings Vol. 11003:
Radar Sensor Technology XXIII
Kenneth I. Ranney; Armin Doerry, Editor(s)

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