论文标题
具有多尺度卷积的CNN，用于使用目标像素方向方案进行高光谱图像分类
A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme
论文作者
论文摘要
最近，CNN是应对高光谱图像分类挑战的流行选择。尽管在超光谱图像（S）（HSI）中具有如此大的光谱信息，但它仍会产生尺寸的诅咒。同样，光谱签名的大空间变异性在分类问题上增加了更困难。此外，通过稀缺的训练示例，在最终以终点为准的CNN是另一个具有挑战性且有趣的问题。在本文中，提出了一种新型的目标点键入方法来训练基于CNN的网络。此外，我们还引入了基于3D-CNN和2D-CNN的网络体系结构的混合体，分别实现了频段减少和特征提取方法。实验结果表明，我们的方法的表现优于现有的最新方法中报告的精度。
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.
