Hyperspectral image classification using 3D 2D CNN

Recent works have shown that deep-learning-derived methods based on convolutional neural network can achieve high performance in terms of accuracy when applied to computer vision task such as object detection, segmentation and classification particularly on hyperspectral image. However, the existing methods have long training times. To reduce the training time and increase the accuracy, this paper proposed a new 3D2D convolutional neural network combined for hyperspectral image classification. For this purpose, a 3D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2D convolutional neural network was introduced to extract spectralspatial features. Four datasets were used for the experiment purpose and the results showed that the proposed method achieved excellent result on both small and large training data compared with existing methods. The proposed method increased the overall accuracies by 2% on UP and KSC datasets while significantly reducing the training time on IP and KSC datasets, respectively. The proposed method increased all accuracies for at least 6% on IP, KSC and UP datasets when compared to some state-of-the-art methods. Also, it reduced considerably the training and testing time on IP and KSC datasets when fast convolution block alone is involved.

Automated summary

This study proposed a new 3D2D convolutional neural network for hyperspectral image classification, which achieved advantages in accuracy and training time compared to existing methods by introducing components such as fast learning block and depthwise separable convolution block to extract spectral-spatial features.