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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >An Enhanced 3-D Discrete Wavelet Transform for Hyperspectral Image Classification
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An Enhanced 3-D Discrete Wavelet Transform for Hyperspectral Image Classification

机译：高光谱图像分类增强的三维离散小波变换

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摘要

In the classification of hyperspectral image (HSI), there exists a common issue that the collected HSI data set is always contaminated by various noise (e.g., Gaussian, stripe, and deadline), degrading the classification results. To tackle this issue, we modify the 3-dimensional discrete wavelet transform (3DDWT) method by considering the noise effect on feature quality and propose an enhanced 3DDWT (E-3DDWT) approach to extract the feature and meanwhile alleviate the noise. Specifically, the proposed E-3DDWT method first applies classical 3DDWT method to the HSI data cube and thus can generate eight subcubes in each level. Then, the stripe noise is concentrated into several subcubes due to its spatial vertical property. Finally, we abandon these subcubes and obtain the feature cube by stacking the remaining ones. After acquiring the feature, we then adopt the convolutional neural network (CNN) model with an active learning strategy for classification since CNN has been verified to be a state-of-the-art feature extraction method for HSI classification, and active learning strategy can alleviate the insufficient labeled sample issue to some extent. In addition, we apply the Markov random field to enhance the final categorized results. Experiments on two synthetically striped data sets show that our proposed approach achieves better categorized results than other advanced methods.
机译：在高光谱图像（HSI）的分类中，存在所收集的HSI数据集始终被各种噪声（例如，高斯，条纹和截止日期）污染的共同问题，降低分类结果。为了解决这个问题，我们通过考虑对特征质量的噪声效果来修改三维离散小波变换（3DDWT）方法，并提出增强的3DDWT（E-3DDWT）方法来提取该功能，同时缓解噪声。具体地，所提出的E-3DDWT方法首先将古典3DDWT方法应用于HSI数据多维数据集，因此可以在每个级别生成八个子机。然后，由于其空间垂直特性，条纹噪声集中成几个子机。最后，我们放弃这些子机器并通过堆叠其余的子机来获取特征立方体。在获取特征后，我们采用卷积神经网络（CNN）模型具有激活的学习策略，以便分类，因为CNN被验证为用于HSI分类的最先进的特征提取方法，并且可以实现主动学习策略在某种程度上减轻了标记的样本问题的不足。此外，我们还应用马尔可夫随机字段来增强最终分类结果。两个合成条带化数据集的实验表明，我们的建议方法实现了比其他高级方法更好的分类结果。

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