Hyper Spectral Image Data Classification Using Deep Learning
Keywords:
capturing, presents, superiority, O-CNN, E-CNN, SVMAbstract
This paper presents a novel framework for hyperspectral image classification, using deep learning techniques to achieve high classification accuracy. The proposed approach integrates convolutional neural networks (CNN) with improved clustering and feature fusion strategies, outperforming traditional methods. By integrating an optimized architecture and clustering strategy, the proposed method effectively addresses the challenges of integrating high-dimensional data and spectral-spatial features. Experiments performed on standard datasets show the superiority of the proposed model, achieving an overall accuracy (OA) of 99.10% for the Indian Pines dataset and 99.09% for the University of Pavia dataset, outperforming other state-of-the-art classifiers such as O-CNN, E-CNN and SVM. These results prove the effectiveness of the model in accurately capturing spatial-spectral features, making it suitable for hyperspectral data analysis tasks.
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