A Transfer Learning Method with Multi-feature Calibration for Building Identification

Traditional building identification methods are difficult for extracting the specific information of various buildings. In this paper, A transfer learning method with multi-feature calibration is proposed for building identification. Our model is based on the pre-training and fine-tuning framework of transfer learning. First, a CNN-based feature extractor, pre-trained by ImageNet, is adopted to extract features, then flatten the feature maps and feed it to a fully-connected network for image classification. This basic transfer learning model can correctly identify 81.2% of test samples. Further, a multi-feature calibration method is proposed. By defining the features of multi-functional buildings artificially, the feature vectors via the extractor are more representative and it can be efficiently applied on some small-sample data sets. We use a self-made building data set to test our methods. The experimental results show that the recognition accurate rate of the model with multi-feature calibration attains to 91.9%