Remote sensing image categorization with domain adaptation-based convolution neural network

With the increasing application of high-resolution remote sensing image, image categorization becomes a more and more important technique. Recently, Convolution Neural Network (CNN) has been widely used in various computer vision tasks, for instance, generic image recognition, object detection and image segmentation. A key factor which influences the performance of CNN is the large quantity of the training images. However, it is hard to obtain large amounts of high-resolution quality images while domain adaptation can be adopted in solving this issue. As a result, in this work, we exploit domain adaptation-based CNN into high-resolution image classification task. Experiments are carried out on a latest large remote sensing image benchmark dataset. Extensive results prove the effectiveness of the proposed model.