Classifying High Resolution Remote Sensing Images by Fine-Tuned VGG Deep Networks

Deep convolutional networks perform well in remote sensing (RS) image classification. Usually, it is difficult to obtain a large number of labeled samples in remote sensing classification tasks. Traditionally, the acquisition of remote sensing images is quite different from the photos provided by digital cameras. However, the imaging system for high resolution (HR) RS images (often with RGB 3 channels) is similar to those provided by digital cameras. In the paper, a transfer learning algorithm based on deep neural networks is proposed to attack the problem of lacking labeled RS samples, in particular on the context of pre-trained deep convolutional networks, i.e., VGGNet. Here, the VGGNet is trained on labeled multimedia images provided by “ImageNet Large Scale Visual Recognition Challenge” (ILSVRC). In the proposed strategy, the VGGNet is adopted as a base classifier, and then labeled RS data samples are exploited to fine-tune higher hidden layers in the 16-layer VGG deep neural networks by the back-propagation algorithm. The proposed method is denoted as RS-VGGNet. The proposed RS-VGGNet is validated by real HR remote sensing images, which were acquired from the National Agriculture Imagery Program(NAIP) dataset. Experimental results show that the RS-VGGNet can achieve a higher accuracy compared to the original VGGNet and shallow machine learning methods. And the proposed RS-VGGNet significantly reduces training times and computing burden as well.