Obtaining training sets for special hyperspectral data sets or applications seems so time consuming and expensive especially for relatively inaccessible locations. Moreover, current techniques of image processing and pattern recognition are not robust enough to make automated remote sensing interpretation feasible. The Margin Infused Relaxed Algorithm (MIRA) is a new perceptron-like online algorithm with a margin-dependent learning rate; meanwhile, it's also a specific online algorithm that seeks a set of prototypes to represent each class. In this paper, we put emphasis on building an online framework by MIRA, which can naturally combine inputs from human and learn as few labeled data points as possible. Experimental results have proved that the MIRA applied in our method is effective in classification problem and economical of the computation time cost.
['Jiming Li', 'Zhenfang Hu', 'Yuntao Qian']
Hyperspectral data classification using Margin Infused Relaxed Algorithm