Classification of small-scale hyperspectral images with multi-source deep transfer learning
Xin Zhao, Yi Liang, Alan J. X. Guo, Fei Zhu
Abstract
Recently, many methods based on deep learning (DL) have been used for hyperspectral image (HSI) classification and achieved good performance. But such approaches often need numerous labelled training samples. This issue is aggravated when applying DL on small-scale HSIs. To alleviate this problem, transfer learning (TL) is introduced to HSI analysis by some existing works. Most of these works transfer knowledge from a single source domain to the target domain. However, the single source TL tends to learn specific knowledge instead of general knowledge. Moreover, since the samples are limited in one source domain, it only partially alleviates the shortage of labelled samples. To learn more general knowledge and further alleviate the issue of limited samples, we introduce the multi-source transfer learning strategy to classify HSIs. Specifically, a framework named multi-source deep transfer learning (MS-DTL) is proposed. This framework consists of a multi-source compatible model and a customized loss function. We perform experiments by comparing the proposed method with the baseline methods on the well-known hyperspectral datasets. The results show that the proposed MS-DTL performs better than the benchmarks on the classification tasks of the small-scale HSIs. Thanks to the strategy of TL, the proposed network is also time-saving.