Classification of High-Spatial-Resolution Remote Sensing Scenes Method Using Transfer Learning and Deep Convolutional Neural Network
Wenmei Li, Ziteng Wang, Yu Wang, Jiaqi Wu, Juan Wang, Yan Jia, Guan Gui
Abstract
The deep convolutional neural network (DeCNN) is considered one of promising techniques for classifying the high-spatial-resolution remote sensing (HSRRS) scenes, due to its powerful feature extraction capabilities. It is well-known that huge high-quality labeled datasets are required for achieving the better classification performances and preventing overfitting, during the training DeCNN model process. However, the lack of high-quality datasets limits the applications of DeCNN. In order to solve this problem, in this article, we propose a HSRRS image scene classification method using transfer learning and the DeCNN (TL-DeCNN) model in a few shot HSRRS scene samples. Specifically, three typical DeCNNs of VGG19, ResNet50, and InceptionV3, trained on the ImageNet2015, the weights of their convolutional layer for that of the TL-DeCNN are transferred, respectively. Then, TL-DeCNN just needs to fine-tune its classification module on the few shot HSRRS scene samples in a few epochs. Experimental results indicate that our proposed TL-DeCNN method provides absolute dominance results without overfitting, when compared with the VGG19, ResNet50, and InceptionV3, directly trained on the few shot samples.