Abstract
Remote sensing images have become one of the most important imaging resources recently. Thus, it is important to develop high-performance techniques to process and manipulate these images. On the other hand, image processing techniques are enhanced spatially based on neural networks. Deep learning is one of the most important techniques in use for computer vision tasks and has been deployed successfully to solve many tasks. But when dealing with remote sensing images, the deep learning method faces two main problems: the underfitting problem, because of the small amount of learning data and the unbalanced receptive field problem, because of the structural stereotype of the remote sensing images. In this paper, we propose to use a complex-valued neural network to segment high-resolution remote sensing images. The proposed network can deal with the problems of remote sensing images by using an ensemble of Complex-Valued Auto-Encoder. Based on an adaptive clustering technique, this network can be used to solve the multi-label segmentation problem of remote sensing images. The proposed method achieves state-of-the-art performance when evaluated on the ISPRS 2D dataset.