ISAR image evaluation method based on visual perception supervised learning

Junhao Tong; Qiang Yang; Liang Shen; Bingning Li; Sisi Chu

doi:10.1117/12.3009516

16 October 2023 ISAR image evaluation method based on visual perception supervised learning

Junhao Tong, Qiang Yang, Liang Shen, Bingning Li, Sisi Chu

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 1280316 (2023) https://doi.org/10.1117/12.3009516
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Inverse synthetic aperture radar (ISAR) image quality assessment is a prerequisite and key for the development and application of ISAR images. In order to solve the problem of low correspondence between objective evaluation indexes and subjective feelings, this paper proposes an algorithm model for evaluation based on a convolutional neural network. Based on the input of the original image, this paper further combines eye-tracking-based thermal maps to construct a dual channel input residual network evaluation algorithm, which improves the evaluation ability of ISAR image quality. The rationality and effectiveness of this method have been proven through experimental testing.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junhao Tong, Qiang Yang, Liang Shen, Bingning Li, and Sisi Chu "ISAR image evaluation method based on visual perception supervised learning", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 1280316 (16 October 2023); https://doi.org/10.1117/12.3009516

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KEYWORDS

Image quality

Data modeling

Deep learning

Education and training

Visualization

Image fusion

Fusion splicing

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