A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme
This work addresses classification challenges in hyperspectral imaging for remote sensing applications, but it is incremental as it builds on existing CNN methods.
The paper tackles hyperspectral image classification by proposing a target-patch-orientation method and a hybrid 3D-CNN/2D-CNN architecture to address dimensionality and limited training data, achieving state-of-the-art accuracy improvements.
Recently, CNN is a popular choice to handle the hyperspectral image classification challenges. In spite of having such large spectral information in Hyper-Spectral Image(s) (HSI), it creates a curse of dimensionality. Also, large spatial variability of spectral signature adds more difficulty in classification problem. Additionally, training a CNN in the end to end fashion with scarced training examples is another challenging and interesting problem. In this paper, a novel target-patch-orientation method is proposed to train a CNN based network. Also, we have introduced a hybrid of 3D-CNN and 2D-CNN based network architecture to implement band reduction and feature extraction methods, respectively. Experimental results show that our method outperforms the accuracies reported in the existing state of the art methods.