Competent scene classification using feature fusion of pre-trained convolutional neural networks

Abstract

In view of the fact that the development of convolutional neural networks (CNN) and other deep learning techniques, scientists have become more interested in the scene categorization of remotely acquired images as well as other algorithms and datasets. The spatial geometric detail information may be lost as the convolution layer thickness increases, which would have a significant impact on the classification accuracy. Fusion-based techniques, which are regarded to be a viable way to express scene features, have recently attracted a lot of interest as a solution to this issue. Here, we suggested a convolutional feature fusion network that makes use of canonical correlation, which is the linear correlation between two feature maps. Then, to improve scene classification accuracy, the deep features extracted from various pre-trained convolutional neural networks are efficiently fused. We thoroughly evaluated three different fused CNN designs to achieve the best results. Finally, we used the support vector machine for categorization (SVM). In the analysis, two real-world datasets UC Merced and SIRI-WHU were employed, and the competitiveness of the investigated technique was evaluated. The improved categorization accuracy demonstrates that the fusion technique under consideration has produced affirmative results when compared to individual networks.