High Spatial Resolution Remote Sensing Scene Classification Method using
Transfer Learning and Deep Convolutional Neural Network
Abstract
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
over-fitting, during the training DeCNN model process. However, the lack
of high quality datasets often limits the applications of DeCNN. In
order to solve this problem, in this paper, we propose a HSRRS image
scene classification method using transfer learning and DeCNN (TL-DeCNN)
model in 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 over-fitting, when compared
with the VGG19, ResNet50 and InceptionV3, directly trained on the few
shot samples.