高光谱遥感影像分类通常基于地物光谱特征,但影像中同时还存在丰富的空间信息。空间信息的有效利用能显著提高图像分类效果。因其具有的特殊结构,卷积神经网络(CNN)已成功地应用在图像分类领域,对二维图像分类具有很好的效果。如何通过深度学习并结合空间光谱信息来提高分类性能是一个关键问题。结合高光谱影像中的空间特征与光谱信息,提出一种适合于高光谱像素级分类的深度学习三维卷积神经网络模型(3D-CNN),并在初始分类的基础上利用多标签条件随机场进行优化。选取三个通用公开高光谱数据集(Indian Pines数据集、Pavia University数据集、Pavia Center数据集)进行测试,结果表明分类优化后精度得到很大提升,总体精度可达98%,Kappa系数达到97.2%。

Hyperspectral remote sensing image classification is usually based on the spectral features of objects, but there are plenty of spatial informations in the images. The effective use of spatial information can significantly improve the image classification effect. Because of the special structure of convolution neural network (CNN), CNN has been successfully applied in the field of image classification, and has a good effect on the classification of two-dimensional images. How to improve classification performance through deep learning combined with spatial-spectral information is a key point. Combining the spatial features and spectral information of hyperspectral images, we have developed a three-dimensional convolution neural network model (3D-CNN) for hyperspectral pixel classification, and the multi labels conditional random field is optimized on the basis of the initial classification. Three general open hyperspectral datasets (Indian Pines dataset, Pavia University dataset, Pavia Center dataset) are selected for testing. Experiments show that the accuracy is greatly improved after the classification optimization, the overall accuracy can reach 98%, and the Kappa coefficient reaches 97.2%.