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Article

Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification

by
Sheng-Chieh Hung
1,†,
Hui-Ching Wu
2,† and
Ming-Hseng Tseng
1,3,4,*
1
Master Program in Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan
2
Department of Medical Sociology and Social Work, Chung Shan Medical University, Taichung 402, Taiwan
3
Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan
4
Information Technology Office, Chung Shan Medical University Hospital, Taichung 402, Taiwan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2021, 11(24), 11659; https://doi.org/10.3390/app112411659
Submission received: 4 November 2021 / Revised: 1 December 2021 / Accepted: 4 December 2021 / Published: 8 December 2021
(This article belongs to the Special Issue Advances in Deep Learning III)

Abstract

Through the continued development of technology, applying deep learning to remote sensing scene classification tasks is quite mature. The keys to effective deep learning model training are model architecture, training strategies, and image quality. From previous studies of the author using explainable artificial intelligence (XAI), image cases that have been incorrectly classified can be improved when the model has adequate capacity to correct the classification after manual image quality correction; however, the manual image quality correction process takes a significant amount of time. Therefore, this research integrates technologies such as noise reduction, sharpening, partial color area equalization, and color channel adjustment to evaluate a set of automated strategies for enhancing image quality. These methods can enhance details, light and shadow, color, and other image features, which are beneficial for extracting image features from the deep learning model to further improve the classification efficiency. In this study, we demonstrate that the proposed image quality enhancement strategy and deep learning techniques can effectively improve the scene classification performance of remote sensing images and outperform previous state-of-the-art approaches.
Keywords: image quality; remote sensing; scene classification; deep learning; explanation artificial intelligence image quality; remote sensing; scene classification; deep learning; explanation artificial intelligence

Share and Cite

MDPI and ACS Style

Hung, S.-C.; Wu, H.-C.; Tseng, M.-H. Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification. Appl. Sci. 2021, 11, 11659. https://doi.org/10.3390/app112411659

AMA Style

Hung S-C, Wu H-C, Tseng M-H. Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification. Applied Sciences. 2021; 11(24):11659. https://doi.org/10.3390/app112411659

Chicago/Turabian Style

Hung, Sheng-Chieh, Hui-Ching Wu, and Ming-Hseng Tseng. 2021. "Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification" Applied Sciences 11, no. 24: 11659. https://doi.org/10.3390/app112411659

APA Style

Hung, S.-C., Wu, H.-C., & Tseng, M.-H. (2021). Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification. Applied Sciences, 11(24), 11659. https://doi.org/10.3390/app112411659

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