R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method
R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method
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R-VCANet：一种新的基于深度学习的高光谱图像分类方法
DOI：
10.1109/jstars.2017.2655516
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发表时间：
2017-02
影响因子：
5.5
通讯作者：
Xu Xia
中科院分区：
文献类型：
--
作者：
Pan Bin;Shi Zhenwei;Xu Xia
基于深度学习的方法在高光谱图像（HSI）分类中表现出了很好的性能，因为它们能够从HSI中提取深度特征。然而，这些方法通常需要大量的训练样本。当样本数量有限时，深度学习模型很难为HSI数据提供具有代表性的特征表达。本文提出了一种新的简化深度学习模型--滚动引导滤波器（RGF）和顶点分量分析网络（R-VCANet），该模型在训练样本数量不丰富的情况下具有更高的精度。在R-VCANet中，HSI数据的固有属性，空间信息和光谱特性，被用来构建网络。通过这种方法，得到的模型可以用较少的样本生成更强大的特征表达。首先，光谱和空间信息相结合，通过RGF，它可以探索上下文结构特征，并删除小细节从HSI。更重要的是，我们设计了一个新的网络称为顶点分量分析网络的深度特征提取平滑HSI。在三个流行的数据集上的实验表明，所提出的基于R-VCANet的方法表现出更好的性能比一些国家的最先进的方法，特别是当可用的训练样本不丰富。
Deep-learning-based methods have displayed promising performance for hyperspectral image (HSI) classification, due to their capacity of extracting deep features from HSI. However, these methods usually require a large number of training samples. It is quite difficult for deep-learning model to provide representative feature expression for HSI data when the number of samples are limited. In this paper, a novel simplified deep-learning model, rolling guidance filter (RGF) and vertex component analysis network (R-VCANet), is proposed, which achieves higher accuracy when the number of training samples is not abundant. In R-VCANet, the inherent properties of HSI data, spatial information and spectral characteristics, are utilized to construct the network. And by this means the obtained model could generate more powerful feature expression with less samples. First, spectral and spatial information are combined via the RGF, which could explore the contextual structure features and remove small details from HSI. More importantly, we have designed a new network called vertex component analysis network for deep features extraction from the smoothed HSI. Experiments on three popular datasets indicate that the proposed R-VCANet based method reveals better performance than some state-of-the-art methods, especially when the training samples available are not abundant.
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DOI：
10.1109/jstars.2012.2194696
发表时间：
2012-04-01
影响因子：
5.5
作者：
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通讯作者：
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DOI：
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发表时间：
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影响因子：
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作者：
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通过深度网络和超像素分割进行高光谱分类
影响因子：
3.4
作者：
Liu, Yazhou;Cao, Guo;Siegel, Mel
通讯作者：
Siegel, Mel