关键词: 高光谱, 深层残差网络, 迁移学习

Abstract: In the field of hyperspectral image classification, the potential of spatial features is just taken into consideration in recent years and yet still not fully exploited. In this work, it generalizes the deep residual network to hyperspectral image classification as a feature extractor which is pre-trained on large-scale common image datasets, the discriminability of extracted features is verified on real data experiments and showed to be very promising. Moreover, under the supervised learning setting, aiming at the problem of overfitting due to insufficient label samples, a model-based transfer learning strategy is proposed. Through pre-training the deep residual network in another related hyperspectral data set, it then fixes the shallow convolution kernel parameters, and uses a small number of labeled samples of the target data set to fine-tune the network top-level convolution kernel parameters. The ability of generalization on new data set is also proved.

Key words: hyperspectral, deep residual network, transfer learning