Classification of Remotely Sensed Images Using an Ensemble of Improved Convolutional Network

In the last few years, the deep learning methods, especially the residual neural network, have achieved impressive performance in remote sensing image recognition tasks. However, there are still specific problems that need to be addressed. It is well known that the first several layers of the network provide much discriminative information, and the ResNet reduces the size of the feature map so quickly that it failed to fully learn the information beneficial to classification in the early stage. Second, insufficient labeling data in remote sensing database may easily lead to overfitting and affect the final classification accuracy. Third, the optimal results cannot be achieved by relying solely on transfer learning. To overcome the problems mentioned earlier, we propose an enhanced residual neural network (ERNet) to improve the classification performance on remote sensing images. We moderately broadened the first several layers of the network, changed the size of the convolution filters, and made it learn more information of image features. Second, we add dropout layer to each residual unit of the proposed network to improve the accuracy and generalization power of ERNet. Finally, an ensemble of learning methods based on ERNet was introduced to improve the classification performance by fusing features of other baseline methods. Extensive experimental results on several benchmark data sets of remote sensing images demonstrate the superior performance of our proposed algorithm.