关键词: 遥感图像, 语义分割, 卷积神经网络, U-Net, 集成学习

Abstract: Image segmentation is an important base-part of remote sensing interpretation. High resolution remote sensing image contains complex object information, but the applications of traditional segmentation methods are greatly limited. The segmentation method, represented by the deep convolution neural network, has made a breakthrough in many fields. Aiming at the problem of high resolution remote sensing image segmentation, this paper proposes a deep convolution neural network based on U-Net, which achieves the end to end pixel level semantic segmentation. It expands the original dataset, trains a binary classification model for every class of objects, and then combines the prediction subgraphs to generate the final semantic segmentation image, which has helped us get 94% training accuracy and 90% test accuracy on the dataset of AI classification and recognition contest of CCF satellite images. The experimental results show that the network not only has good generalization ability but also can be used in practical engineering with high segmentation accuracy.

Key words: remote sensing image, semantic segmentation, convolutional neural network, U-Net, ensemble learning