Scene Recognition in New Multi-Spectral Dataset (RGB-IR) and Deep Convolutional Networks with Transfer Learning Approach

In recent decades, various techniques in the field of computer vision have been proposed to classify and identify scenes in different spaces on visible spectrum images. Recently, improvements in various areas of machine vision using complementary features in visible and infrared images have been reported by researchers. This approach can also work for the subject of scene recognition. In this paper, we first create a multispectral image database, including pairs of color-visible and infrared spectrum images. Then, the images are fused by wavelet transform and using a weighting method based on training the deep convolutional neural networks. This approach is also compared with several other fusion methods using quantitative evaluation criteria. Finally, images of different scenes are classified using architectures based on deeply trained convolutional neural networks. Transfer learning approach is also used to classify the scene with the least computational cost. Experimental results show that the proposed method has been effective in scene classification, which is done by fusion of four-channel images (RGB-IR) with a classification accuracy of 96.67%. This method also shows better quantitative fusion criteria than other multispectral image fusion approaches.