Iterative weighted active transfer learning hyperspectral image classification

The hyperspectral image (HSI) has the characteristics of high resolution, a large amount of data, and a high correlation of bands. In the many HSI processing technologies, image classification is the most basic one. Supervised classification is the most effective and common classification method. However, to achieve the ideal classification effect, supervised classification needs a large number of labeled samples, which requires a lot of time and labor. To solve this problem, we combine active learning (AL) and transfer learning and propose an iterative weighted framework based on active transfer learning. First, we solve the optimal reconstruction matrix and projection matrix by minimizing the reconstruction error. Then, we project labeled samples from the source and target domains into the common subspace. In the iteration of the common subspace, the classifier performance will be better with the increase of iteration number. In each iteration, the weighted strategy is adopted to weigh the samples of the source domain. In this way, valuable source domain labeled samples will get a larger weight, so as to help the classification of target domain samples better. At the same time, AL is used to screen out a certain number of samples of the target domain for manual labeling, which is added to the labeled samples set. Experiments on three data sets demonstrate the effectiveness and reliability of the proposed method. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Automated summary

The study proposes an iterative weighted framework that combines active learning and transfer learning to address the issue of large labeled sample requirements in supervised classification. Experimental results demonstrate the effectiveness and reliability of the method.