No abstract

In the recent decades, various techniques based on deep convolutional neural networks (DCNNs) have been applied to scene classification. Most of the techniques are established upon single-spectral images such that environmental conditions may greatly affect the quality of images in the visible (RGB) spectrum. One remedy for this downside is to merge the infrared (IR) with the visible spectrum for gaining the complementary information in comparison with the unimodal analysis. This paper incorporates the RGB, IR and near-infrared (NIR) images into a multispectral analysis for scene classification. For this purpose, two strategies are adopted. In the first strategy, each RGB, IR and NIR image is separately applied to DCNNs and then classified according to the output score of each network. In addition, an optimal decision threshold is obtained based on the same output score of each network. In the second strategy, three image components are extracted from each type of image using wavelet transform decomposition. Independent DCNNs are then trained on the image components of all the scene classes. Eventually, the final classification of the scene is accomplished through an appropriate ensemble architecture. The use of this architecture alongside a transfer learning approach and simple classifiers leads to lesser computational costs in small datasets. These experiments reveal the superiority of the proposed method over the state-of-the-art architectures in terms of the accuracy of scene classification.