LAUSR

With the increasing popularity of deep learning, most recent developments of change detection (CD) approaches have taken advantage of deep learning techniques to improve the detection performance. However, it is usually necessary to elaborately design the network architecture and train the model with a large amount of labeled data, which are difficult to obtain in practice. To overcome these limitations, this letter proposed an unsupervised CD framework for high-resolution remote sensing images integrating transfer learning-based bilinear convolutional neural networks (BCNNs) and object-based change analysis. A difference image is first generated, which is used for the subsequent preclassification and superpixel segmentation. Then, two sets of superpixel samples with reliable labels derived from the bitemporal remote sensing images are input into two pretrained CNNs to extract representative features, respectively. On this basis, the matrix outer product is utilized to generate the combined bilinear features, which are input into the softmax classifier to discriminate the change and no-change information and thus obtaining the final change map by feeding all sample data into the well-trained model. The experimental results on three real data sets demonstrate the effectiveness and superiority of the proposed method over several existing CD approaches.