A Kernel-Based Extreme Learning Machine Framework for Classification of Hyperspectral Images Using Active Learning

The rapid development of advanced remote sensing technology with multichannel imaging sensors has increased its potential opportunity in the utilization of hyperspectral data for various applications. For supervised classification of hyperspectral data, obtaining suitable training set is essential for ensuring good performance. However, obtaining labeled training sample is often difficult, expensive, and time consuming in hyperspectral images (HSIs) and other image analysis applications. To overcome this problem, active learning (AL) technique plays a crucial role in the image analysis framework. As per literature, classification of HSI using AL has been focused in terms of accuracy, but learning rate in terms of computation time has not been focused yet. In this paper, multiview-based AL technique has been integrated with kernel-based extreme learning machine (KELM) classifier. Further, we have compared our approach with popularly used kernel-based support vector machine (KSVM). To validate our study, experiments were conducted on two Hyperspectral Images: Kennedy Space Centre (KSC) and Botswana (BOT) datasets. The proposed approach (KELM-AL) achieved the classification accuracy up to 91.15% in KSC dataset while 95.02% in case of BOT dataset with computation time of 149.78 s and 104.98 s, respectively. While KSVM-AL achieved the classification accuracy up to 91.59% in KSC dataset while 95.96% in case of BOT dataset with computation time of 7532.25 s and 6863.60 s, respectively. This shows that classification accuracy obtained by KELM-AL is comparable to KSVM-AL approach but significantly reduces the computational time. Thus, the proposed system shows the promising results with adequate classification accuracy while reducing the computation time drastically.