Phase-Induced Gabor-Based Multiview Active Learning for Hyperspectral Image Classification

In this letter, we propose a new phase-induced Gabor-based multiview active learning (MVAL) (PGMVAL) approach for hyperspectral image (HSI) classification. Our main contribution is to explore the potential of the phase offset term $P$ in hand-crafted Gabor feature extraction, which is rarely exploited in previous works. The Gabor filters with $P$ added, named as the phase-induced Gabor filters, are able to adjust their frequency response characteristics through $P$ . Specifically, we utilize the phase-induced Gabor filtering for view generation purposes under a MVAL framework. As a result, PGMVAL is capable to exploit the complementary information residing in the phase-induced Gabor features corresponding to different $P\text{s}$ and simultaneously avoids high memory consumption and a large number of training samples required caused by introducing a new parameter. The experimental results obtained on two benchmark HSI data sets show that the proposed PGMVAL approach using phase-induced Gabor filtering could achieve better classification results with limited training samples.