Abstract:

How to recognize targets with similar appearances from remote sensing images (RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network (CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However, the training and testing of CNN mainly rely on single machine. Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure. When a model is complex or the training data is relatively small, overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore, Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Naïve Bayes classifier, a distributed Naïve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.

Key words: convolutional neural network (CNN), distributed architecture, remote sensing images (RSIs), target classification, pre-training