Abstract
Over the past few decades, hyperspectral image (HSI) classification has garnered increasing attention from the remote sensing research community. The largest challenge faced by HSI classification is the high feature dimensions represented by the different HSI bands given the limited number of labeled samples. Deep learning and convolutional neural networks (CNNs), in particular, have been shown to be highly effective in several computer vision problems such as object detection and image classification. In terms of accuracy and computational cost, one of the best CNN architectures is the Inception model i.e., the winner of the ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2014 challenge. Another architecture that has significantly improved image recognition performance is the Residual Network (ResNet) architecture i.e., the winner of the ILSVRC 2015 challenge. Inspired by the incredible performance introduced by the Inception and ResNet architectures, we investigate the possibility of combining the core ideas of these two models into a hybrid architecture to improve the HSI classification performance. We tested this combined model on four standard HSI datasets, and it shows competitive results compared with other existing HSI classification methods. Our hybrid deep ResNet-Inception architecture obtained accuracies of 95.31% on the Pavia University dataset, 99.02% on the Pavia Centre scenes dataset, 95.33% on the Salinas dataset and 90.57% on the Indian Pines dataset.