Hyperspectral Image Classification With Deep Learning Models

Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.

# Remote-Sensing Image Classification # Engineering # Advanced Neural Network Applications # Computer Science # Advanced Image Fusion Techniques

Xiaofei Yang, Yunming Ye, Xutao Li, et al. (2018). "Hyperspectral Image Classification With Deep Learning Models". IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2018.2815613