Abstract
Currently, how to efficiently exploit useful information from multi-source remote sensing data for better Earth observation becomes an interesting but challenging problem. In this paper, we propose an collaborative classification framework for hyperspectral image (HSI) and Light Detection and Ranging (LIDAR) data via image-to-image convolutional neural network (CNN). There is an image-to-image mapping, learning a representation from input source (i.e., HSI) to output source (i.e., LIDAR). Then, the extracted features are expected to own characteristics of both HSI and LIDAR data, and the collaborative classification is implemented by integrating hidden layers of the deep CNN. Experimental results on two real remote sensing data sets demonstrate the effectiveness of the proposed framework.
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