COLLABORATIVE CLASSIFICATION OF HYPERSPECTRAL AND LIDAR DATA WITH INFORMATION FUSION AND DEEP NETS

Convolutional neural network (CNN) receives extensive attention in hyperspectral image classification. While hyperspectral images contain abundant spectral information but lack spatial information, which usually contributes to poor classification results. In this paper, a novel classification framework called information fusion based CNN (IF-CNN) is proposed to compensate for the shortcomings of hyperspectral images. The proposed method merges hyperspectral images with abundant spectral information and LiDAR images with rich spatial information as the input of classification framework. Furthermore, the framework consists of two convolutional neural networks: one-dimensional CNN for extracting spectral features, and two-dimensional CNN for extracting spatial correlation features. Experimental results demonstrate that the proposed method achieves excellent performance compared with some existing methods.