High-Resolution Remote Sensing Image Scene Classification by Merging Multilevel Features of Convolutional Neural Networks

Abstract

The rapid development of remote sensing technologies has yielded a large amount of high-resolution remote sensing (HRRS) data. However, effectively extracting information from these large datasets is a significant challenge. In this study, a state-of-the-art convolutional neural network was extended for HRRS image classification by establishing an integrated structure including three branches of a pruned DenseNet, a decoder, and an encoder (PDDE-Net). PDDE-Net initially combines deep-level features extracted from the pruned DenseNet with middle-level features extracted from the designed encoder. Subsequently, the designed decoder branch extracts the merged features to obtain more feature expressions. In addition, the earlystop strategy is used to prevent overfitting during the training process. Experiments performed on an aerial image dataset demonstrated that the proposed network can achieve favorable overall accuracy and Kappa values of 90% and 0.897, respectively. Further, under the condition of imbalanced data, two merging methods—concatenation and summation—were applied to test the sensitivity of the integrated PDDE-Net to the feature size. The results on the remote sensing image classification benchmark datasets revealed little difference between the two merging methods, both of which exhibited accuracies greater than 95%. Moreover, the integrated PDDE-Net was tested on hyperspectral remote sensing data, and it achieved a high classification accuracy comparable to those of other advanced methods, which also demonstrated the generalizability of the proposed model.