Ran Lingyan, Zhang Yanning, Wei Wei, Zhang Qilin
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
Highly Automated Driving Team, HERE Technologies Automotive Division, Chicago, IL 60606, USA.
Sensors (Basel). 2017 Oct 23;17(10):2421. doi: 10.3390/s17102421.
During recent years, convolutional neural network (CNN)-based methods have been widely applied to hyperspectral image (HSI) classification by mostly mining the spectral variabilities. However, the spatial consistency in HSI is rarely discussed except as an extra convolutional channel. Very recently, the development of pixel pair features (PPF) for HSI classification offers a new way of incorporating spatial information. In this paper, we first propose an improved PPF-style feature, the spatial pixel pair feature (SPPF), that better exploits both the spatial/contextual information and spectral information. On top of the new SPPF, we further propose a flexible multi-stream CNN-based classification framework that is compatible with multiple in-stream sub-network designs. The proposed SPPF is different from the original PPF in its paring pixel selection strategy: only pixels immediately adjacent to the central one are eligible, therefore imposing stronger spatial regularization. Additionally, with off-the-shelf classification sub-network designs, the proposed multi-stream, late-fusion CNN-based framework outperforms competing ones without requiring extensive network configuration tuning. Experimental results on three publicly available datasets demonstrate the performance of the proposed SPPF-based HSI classification framework.
近年来，基于卷积神经网络（CNN）的方法主要通过挖掘光谱变异性，被广泛应用于高光谱图像（HSI）分类。然而，除了作为额外的卷积通道外，HSI中的空间一致性很少被讨论。最近，用于HSI分类的像素对特征（PPF）的发展提供了一种纳入空间信息的新方法。在本文中，我们首先提出一种改进的PPF风格特征，即空间像素对特征（SPPF），它能更好地利用空间/上下文信息和光谱信息。在新的SPPF之上，我们进一步提出了一个灵活的基于多流CNN的分类框架，该框架与多种流内子网设计兼容。所提出的SPPF在其配对像素选择策略上与原始PPF不同：只有紧邻中心像素的像素才有资格，因此施加了更强的空间正则化。此外，通过现成的分类子网设计，所提出的基于多流、后期融合CNN的框架在无需广泛调整网络配置的情况下优于竞争框架。在三个公开可用数据集上的实验结果证明了所提出的基于SPPF的HSI分类框架的性能。
