Vehicle detection in remote sensing images using denoizing-based convolutional neural networks
Hao Li, Kun Fu, Menglong Yan, Xian Sun, Hao Sun, Wenhui Diao · Remote Sensing Letters · 2016
Vehicle detection in remote sensing images is a tough task and of great significance due to the fast increasing number of vehicles occurring in big cities. Recently, convolutional neural network (CNN)-based methods have achieved excellent performance in classification task due to their powerful abilities in high-level feature extraction. However, overfitting is a serious problem in CNN when applying complicated fully-connected layers, especially when the quantity of training samples is limited. In order to tackle this problem, a denoizing-based CNN called DCNN is proposed in this letter. More specially, a CNN with one fully-connected layer is pre-trained first for feature extraction. After that, features of this fully-connected layer are corrupted and used to pre-train a stacked denoizing autoencoder (SDAE) in an unsupervised way. Then, the pre-trained SDAE is added into the CNN as the fully-connected layer. After fine-tuning, DCNN can make the extracted features more robust and the detecting rate higher. With the help of our proposed locating method, vehicles can be detected effectively even when they are parked in a residential area. Comparative experiments demonstrate that our method has achieved state-of-the-art performance.