A semi-supervised cycle-GAN neural network for hyperspectral image classification with minimum noise fraction

Author:

Reddy Tatireddy1ORCID,Harikiran Jonnadula2

Affiliation:

1. Research Scholar, School of Computer Science and Engineering, VIT-AP University, Amaravathi, Andhra Pradesh-522237, India and Assistant Professor, Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, Telangana, India. tatireddysubba12@gmail.com

2. Associate Professor, School of Computer Science and Engineering, VIT-AP University, Amaravthi, Andhra Pradesh-522237, India. jonnadulaharikiran@gmail.com

Abstract

Hyperspectral imaging (HSI) is a popular mode of remote sensing imaging that collects data beyond the visible spectrum. Many classification techniques have been developed in recent years, since classification is the most crucial task in hyperspectral image processing. Furthermore, extracting features from hyperspectral images is challenging in many scenarios. The semi-supervised classification of HSI is motivated by the Cycle-GAN method that has been proposed in this research paper. Since the proposed HSI classification method is semi-supervised, it makes extensive use of the labelled samples, which are short and have numerous unlabelled images. The research is carried out in two phases. First, to extract the spectral–spatial features, the minimum noise fraction is adopted. And, second, the classification of the semi-supervised method is done by the cycle-GANs. Subsequently, the proposed architecture is implemented on three standard hyperspectral dataset methods. As a result, the performance comparison is carried out in the same field as state-of-the-art approaches. The obtained results successfully demonstrate the supremacy of the proposed technique in the classification of HSI.

Publisher

IM Publications Open LLP

Subject

Spectroscopy,Analytical Chemistry

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review;Sensors;2024-05-08

2. An Efficient PAN-sharpening of Multispectral Images using Multi-scale Residual CNN with Sparse Representation;2024 International Conference on Integrated Circuits and Communication Systems (ICICACS);2024-02-23

3. Hyperspectral Image Classification based on Cycle GAN and EfficientNet;2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT);2024-01-11

4. Two-Stream Networks for Contrastive Learning in Hyperspectral Image Classification;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

5. DesU-NetAM: optimized DenseU-Net with attention mechanism for hyperspectral image classification;International Journal of Information Technology;2023-08-25

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