Optimization of Natural Landscape Images Using CNN and Improved U-Net Technology

Qinyi Yin

2024

Abstract

Natural landscape images are of great significance in many fields such as computer vision and environmental protection, and have a high degree of diversity and complexity. Therefore, zoning optimization for these images is crucial. In this paper, the Convolutional Neural Networks (CNN) are used to classify the images and the improved U-Net is used to segment the images. This paper first introduces the processing method of CNN, introduces the basic concepts, introduces the structure diagram and other content. Secondly, it introduces the improvement method of U-Net and the optimized structure. Then it makes a comparative analysis of different methods. Through the comparative analysis, this paper finds that CNN can classify images more accurately and U-Net can segment images more clearly. However, it also points out the limitations of convolutional neural network for more complex images and the complexity of U-Net after improvement, such as increasing the consumption of computing resources. The experimental results show that the above summarized methods improve the accuracy of image classification and segmentation, and provide a good basis for the optimization of natural landscape image segmentation.

Paper Citation

in Harvard Style

Yin Q. (2024). Optimization of Natural Landscape Images Using CNN and Improved U-Net Technology. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 334-339. DOI: 10.5220/0013516700004619

in Bibtex Style

@conference{daml24,
author={Qinyi Yin},
title={Optimization of Natural Landscape Images Using CNN and Improved U-Net Technology},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={334-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013516700004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Optimization of Natural Landscape Images Using CNN and Improved U-Net Technology
SN - 978-989-758-754-2
AU - Yin Q.
PY - 2024
SP - 334
EP - 339
DO - 10.5220/0013516700004619
PB - SciTePress