Multispectral scene recognition based on dual convolutional neural networks

Multispectral sensors are becoming more accessible which draws additional attention to the problem of processing and classification of multispectral images. In this research we addressed the problem of automatic scene recognition of multispectral images using convolutional neural networks with tailored architecture. More precisely, we propose and describe a special dual network architecture which is able to efficiently process multispectral images and, at the same time, use the possibilities of networks pretrained on feature-rich image dataset. Experiments showed that dual network can efficiently recognize multispectral scenes, even though a small amount of training images had been available. Comparing to the best accuracy of descriptor based method previously reported, our method made an improvement of nearly 5%, achieving the classification accuracy over 92% on benchmark multispectral scene dataset.