Revolutionizing hyperspectral image classification for limited labeled data: unifying autoencoder-enhanced GANs with convolutional neural networks and zero-shot learning (original) (raw)
Abstract
Hyperspectral image classification grapples with the twin challenges of high dimensionality and limited labelled data. These limitations hinder the development of generalizable classification models that can perform well across diverse datasets. To overcome these limitations, this paper proposes a novel semi-supervised framework that synergizes autoencoders, generative adversarial networks and zero-shot learning. This semi-supervised approach significantly improves feature extraction and data augmentation by harnessing the power of generative adversarial networks built upon autoencoders, ultimately enhancing classification accuracy. It further pushes the boundaries beyond traditional methods by enabling zero-shot learning, allowing the model to classify unseen data from classes not present in the training set. Additionally, the proposed model incorporates text embeddings to enrich feature representation, resulting in improved performance. This multimodal classification approach empowers the way for robust training and testing on cross-sensor datasets, even handling data with diverse spectra. Experimentally, it demonstrates remarkable accuracy across various domains, achieving a peak performance of 92.35% for cross-domain data and 91.83% for same-domain data, marking a significant leap forward in the generalizability of semi-supervised classification models.
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Authors and Affiliations
- School of Information Technology, Murdoch University, Dubai, UAE
Pallavi Ranjan - Computer Science and Engineering, Delhi Technological University, Delhi, India
Anukriti Kaushal & Rajeev Kumar - Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, India
Ashish Girdhar
Authors
- Pallavi Ranjan
- Anukriti Kaushal
- Ashish Girdhar
- Rajeev Kumar
Contributions
Pallavi Ranjan conceived the idea, performed implementation, conducted experiments, and wrote the original manus-cript. Anukriti Kaushal worked on methodology. Rajeev Kumar performed writing - review and editing. Ashish Girdhar identified the problem and guided the project.
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Correspondence toPallavi Ranjan.
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Communicated by: Hassan Babaie.
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Ranjan, P., Kaushal, A., Girdhar, A. et al. Revolutionizing hyperspectral image classification for limited labeled data: unifying autoencoder-enhanced GANs with convolutional neural networks and zero-shot learning.Earth Sci Inform 18, 216 (2025). https://doi.org/10.1007/s12145-025-01739-7
- Received: 27 November 2024
- Accepted: 24 January 2025
- Published: 30 January 2025
- Version of record: 30 January 2025
- DOI: https://doi.org/10.1007/s12145-025-01739-7