Convolutional Neural Network-Based Transfer Learning for Optical Aerial Images Change Detection

Abstract

Considering the lack of labeled training data sets for the supervised change detection task, in this letter, we try to relieve this problem by proposing a convolutional neural network (CNN)-based change detection method with a newly designed loss function to achieve transfer learning among different data sets. To reach this goal, we first pretrain a U-Net model on an open source data set by taking advantages of the relatively sufficient training data used for the supervised semantic segmentation task. Then, we minimize a skillfully designed loss function to combine the high-level features extracted from the pretrained model and the semantic information contained in the change detection data set, by which a transfer learning is achieved. Third, we compute the distance between the feature vectors obtained from the above step and produce a difference map. Finally, a simple clustering method used on the difference map can even obtain satisfied change map. Experiments carried out on typical optical aerial image data sets validate that the proposed approach compares favorably to the state-of-the-art unsupervised methods.