Improving the Accuracy of Binarized Neural Networks and Application on Remote Sensing Data

Deep neural networks are well known to achieve outstanding results in many domains. Recently, many researchers have introduced deep neural networks into remote sensing (RS) data processing. However, typical RS data usually possesses enormous scale. Processing RS data with deep neural networks requires a rather demanding computing hardware. Most high-performance deep neural networks are associated with highly complex network structures with many parameters. This restricts their deployment for real-time processing in satellites. Many researchers have attempted overcoming this obstacle by reducing network complexity. One of the promising approaches able to reduce network computational complexity and memory usage dramatically is network binarization. In this letter, through analyzing the learning behavior of binarized neural networks (BNNs), we propose several novel strategies for improving the performance of BNNs. Empirical experiments prove these strategies to be effective in improving BNN performance for image classification tasks on both small- and large-scale data sets. We also test BNN on a remote sense data set with positive results. A detailed discussion and preliminary analysis of the strategies used in the training are provided.