Adaptive Feature Fusion with Semi-Supervised GAN

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1. Adaptive Feature Fusion with Semi-Supervised GAN Paper ID : 246 M.Dharani Kumar Department of Computer Science and Engineering

2. Abstract Hyperspectral images are vital in remote sensing, agriculture, and environmental monitoring. High dimensionality and lack of labeled data hinder classification accuracy. Proposed method: Adaptive feature fusion with semi-supervised GAN. Combines spectral and spatial features using CNNs and attention mechanisms. Achieves up to 98.5% accuracy with limited labeled samples.

3. Introduction Hyperspectral Imaging provides spectral-spatial information. Challenges: High dimensionality, limited labeled data, noise. Conventional methods like SVMs struggle with these challenges. Proposed solution uses adaptive feature fusion and GANs.

4. Significance of the Research Addresses critical challenges in hyperspectral image classification: High data dimensionality and limited labeled samples. Complex interactions between spectral and spatial features. Noise and variability in real-world datasets. Enhances classification accuracy and robustness: Combines spectral and spatial information adaptively using attention mechanisms. Leverages unlabeled data with semi-supervised GANs to improve generalization. Reduces dependency on costly labeled datasets: Generates synthetic data to augment training. Enables better performance with fewer labeled samples, making it practical for large-scale applications. Applicable to multiple domains: Agriculture, environmental monitoring, urban planning, and disaster management. Facilitates informed decision-making and efficient resource management.

5. Methods / Approach System Design Overview Objective: Robust classification of hyperspectral images despite limited labeled data. Efficient use of spectral and spatial information for better feature extraction. Methodology Highlights: Input Preprocessing: Hyperspectral images are normalized and reshaped to standardize pixel intensities. Ensures compatibility with deep learning models and stabilizes training. Feature Extraction: Convolutional Neural Networks (CNNs) extract hierarchical spectral and spatial features from the image. Captures intricate patterns across multiple spectral bands.

6. Methods / Approach Semi-Supervised GAN Integration: Generator: Creates synthetic hyperspectral samples from noise. Discriminator: Distinguishes between real and synthetic samples while learning classification patterns. Improves generalization by leveraging both labeled and unlabeled datasets. Classification Layer: Softmax layer assigns class labels to each pixel based on the fused features. Provides probabilistic outputs for accurate classification. Training Strategy: Combines adversarial loss (for generating realistic data) with classification loss (for learning from labeled data). Trains on both labeled and unlabeled samples to maximize performance.

7. Discussion / Conclusion Significance of the Results The proposed method achieves significantly higher accuracy, precision, recall, and F1-score compared to conventional models (SVM and CNN), validating its robustness. By effectively combining spectral and spatial features through attention-based fusion , the model accurately classifies hyperspectral images even with limited labeled data. The integration of semi-supervised GANs enhances the model’s ability to generalize, reducing reliance on costly labeled datasets. These results open new possibilities for applying deep learning to real-world hyperspectral data classification tasks.

8. Potential Applications 🌱 Agriculture: Monitoring crop health, soil quality, and nutrient distribution for precision farming. 🌍 Environmental Monitoring: Tracking land use changes, water quality, and pollution levels. 🏙 Urban Planning: Mapping infrastructure, detecting anomalies, and planning sustainable cities. ⚙ Disaster Management: Identifying affected areas during floods, droughts, or wildfires to support quick response efforts.

9. Future Work 🚀 Explore advanced attention mechanisms like transformers to further improve feature selection. 🔬 Investigate GAN variants (e.g., Wasserstein GAN, StyleGAN ) to generate higher-quality synthetic samples. 📈 Scale the framework for real-time processing and large-scale deployment in practical applications. 🧠 Incorporate explainable AI techniques to interpret how the model prioritizes spectral-spatial features during classification.

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11. 7. Liang, H., Bao, W., & Shen, X. (2021). Adaptive weighting feature fusion approach based on generative adversarial network for hyperspectral image classification. Remote Sensing , 13(2), 198. 8.Zhang, M., Wang, Z., Wang, X., Gong, M., Wu, Y., & Li, H. (2023). Features kept generative adversarial network data augmentation strategy for hyperspectral image classification. Pattern Recognition , 142, 109701. 9. Bai, J., Zhang, E., Yang, L., Li, X., & Zhang, S. (2024, June). Generative Adversarial Network-Based Spectral–Spatial Feature Learning for Hyperspectral Image Classification. In 2024 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. 10. Lu, T., Fang, Y., Fu, W., Ding, K., & Kang, X. (2024). Dual-stream class-adaptive network for semi-supervised hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing . 11. Huo , X., Zhang, Y., & Wu, S. (2024). Semi-supervised class-conditional image synthesis with Semantics-guided Adaptive Feature Transforms. Pattern Recognition , 146, 110022. 12.He, Z., Xia, K., Ghamisi , P., Hu, Y., Fan, S., & Zu, B. (2022). HypervitGAN : Semi-supervised generative adversarial network with transformer for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 15, 6053-6068.

12. THANK YOU