Online Active Extreme Learning Machine with Discrepancy Sampling for PolSAR Classification

The extreme learning machine (ELM) has drawn increasing attention in the field of machine learning due to its high accuracy and efficient learning. However, classical ELM works in batch and passive learning paradigms, which cannot deal with sequential data effectively. ELM has been extended to online sequential learning form (OS-ELM) and active learning form (AL-ELM), in which the former is used to improve training efficiency and the latter is mainly adopted to improve accuracy. In order to solve the problem of labeling samples difficulty and costly, poor sample validity, and continuous iterative learning in polarimetric synthetic aperture radar (PolSAR) image classification, we propose an online active extreme learning machine (OA-ELM) algorithm to combine the strengths and make up the weaknesses of OS-ELM and AL-ELM, which improves both efficiency and generalization ability. OA-ELM can learn from sequential data dynamically with low computational complexity and good generalization ability. Specifically, OA-ELM reduces time and memory cost for training via extended recursive least squares for optimization. It also improves accuracy using informative training samples selected by proposed discrepancy sampling (DS), which modifies an active query method called margin sampling (MS). Before applying MS to ELM, real-valued outputs of ELM need to be converted into probabilistic outputs first. Instead, the proposed DS can be applied to ELM directly by calculating the difference between the two largest actual nonprobabilistic outputs of ELM. Experimental results of PolSAR classification demonstrate that OA-ELM is effective and efficient compared with other algorithms in terms of accuracy and running time.