IEEE Trans Image Process. 2017 Oct;26(10):4843-4855. doi: 10.1109/TIP.2017.2725580. Epub 2017 Jul 11.
In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets.
在本文中，我们描述了一种新颖的深度卷积神经网络（CNN），它比其他现有的用于高光谱图像分类的深度网络更深、更宽。与当前基于 CNN 的高光谱图像分类的最新方法不同，所提出的网络称为上下文深度 CNN，可以通过联合利用相邻个体像素向量的局部空间-光谱关系来最佳地探索局部上下文交互。通过用作所提出的 CNN 管道的初始组件的多尺度卷积滤波器组来实现对空间-光谱信息的联合利用。然后将从多尺度滤波器组获得的初始空间和光谱特征图组合在一起，以形成联合空间-光谱特征图。表示高光谱图像丰富光谱和空间特性的联合特征图然后通过全卷积网络进行传递，最终预测每个像素向量的相应标签。该方法在三个基准数据集上进行了测试：印第安纳松树数据集、萨利纳斯数据集和帕维亚大学数据集。性能比较表明，与三个数据集上的最新方法相比，该方法的分类性能得到了提高。
