Grouped Subspace Linear Semantic Alignment for Hyperspectral Image Transfer Learning

Abstract

Transfer learning (TL) offers an effective way to reduce the demand for labeled samples in remote sensing image classification. However, existing TL methods have some limitations. Simple linear TL methods cannot align the source and target domains well when the data shift is complicated, while nonlinear methods consist of many learnable parameters and often need many labeled samples. To overcome these issues, we design a novel grouped subspace linear semantic alignment (G-SLSA) algorithm, which consists of four main ingredients. First, inspired by the linear supervised transfer learning (LSTL) approach, we propose subspace linear semantic alignment (SLSA) aiming to reduce the demand for labeled samples. Second, considering the heterogeneity of class-level data shift, we extend SLSA to G-SLSA through a grouped alignment strategy, which can reduce the data shift by decomposing a multiclass transfer learning task into a set of binary subtasks. Third, considering the demand of subtasks fusion on posterior probabilities, we propose a robust posterior probability estimation method for the binary generalized learning vector quantization (GLVQ) that is used in G-SLSA. Finally, a pairwise coupling (PWC) method is applied to fuse the results of each subtask. Experimental results conducted on three popular hyperspectral datasets demonstrate that G-SLSA outperforms other traditional and state-of-the-art deep learning (DL) methods, with an OA of 80.78±4.28% for PC-UP transfer learning scenario when 5 samples per class are available in the target domain.