Multilayer Feature Fusion With Weight Adjustment Based on a Convolutional Neural Network for Remote Sensing Scene Classification

Remote sensing scene classification is still a challenging task. Extracting features effectively from restricted existing labeled data is key to scene classification. Convolutional neural networks (CNNs) are an effective method of constructing discriminating feature representation. However, CNNs usually utilize the feature map from the last layer and ignore additional layers with valuable feature information. In addition, the direct integration of multiple layers brings only a small improvement due to feature redundancy and destruction. To explore the potential information from additional layers and improve the effect of feature fusion, we propose multilayer feature fusion accesses with weight adjustment based on a CNN. We construct access to deliver additional features to one layer to achieve feature fusion and set weight factors to adjust the fusion degree to reduce feature redundancy and destruction. We perform experiments on two common data sets, which indicate improved accuracies and advantages of the extraction capability of our method.