Transfer Learning with Fully Pretrained Deep Convolution Networks for Land-Use Classification

Abstract

In recent years, transfer learning with pretrained convolutional networks (CNets) has been successfully applied to land-use classification with high spatial resolution (HSR) imagery. The commonly used transfer CNets partially use the feature descriptor part of the pretained CNets, and replace the classifier part of the pretrained CNets in the old task with a new one. This causes the separation and asynchrony between the feature descriptor part and the classifier part of the transferred CNets during the learning process, which reduces the effectiveness of the training process. To overcome this weakness, a transfer learning method with fully pretrained CNets is proposed in this letter for the land-use classification of HSR images. In the proposed method, a multilayer perceptron (MLP) classifier is quickly pretrained using the high-level features extracted by the feature descriptor of the pretrained CNets. Fully pretrained CNets can be generated by concatenating the feature descriptor of the pretrained CNets and the pretained MLP. Because both the feature descriptor and the classifier are pretrained, the separation and asynchrony between the two parts can be avoided during the training process. The final transferred CNets are then obtained by fine-tuning the fully pretrained CNets with the random cropping and mirroring strategy. The experiments show that the proposed method can accelerate the convergence of the training process with no loss of accuracy in land-use classification, and its performance is comparable to other latest methods.