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首页> 外文期刊>Remote sensing letters >A novel spectral-spatial classification technique for multispectral images using extended multi-attribute profiles and sparse autoencoder
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A novel spectral-spatial classification technique for multispectral images using extended multi-attribute profiles and sparse autoencoder

机译：使用扩展的多属性配置文件和稀疏自动编码器的多光谱图像光谱空间分类新技术

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摘要

Image classification is a prominent topic and a challenging task in the field of remote sensing. Recently many various classification methods have been proposed for satellite images specifically the frameworks based on spectral-spatial feature extraction techniques. In this paper, a feature extraction strategy of multispectral data is taken into account in order to develop a new classification framework by combining Extended Multi-Attribute Profiles (EMAP) and Sparse Autoencoder (SAE). Extended Multi-Attribute Profiles is employed to extract the spatial information, then it is joined to the original spectral information to describe the spectral-spatial property of the multispectral images. The obtained features are fed into a Sparse Autoencoder as input. Finally, the learned spectral-spatial features are embedded into the Support Vector Machine (SVM) for classification. Experiments are conducted on two multispectral (MS) images such as we construct the ground truth maps of the corresponding images. Our approach based on EMAP and deep learning (DL), proves its huge potential to achieve a high classification accuracy in reasonable running time and outperforms traditional classifiers and others classification approaches.
机译：在遥感领域，图像分类是一个突出的主题，也是一项艰巨的任务。最近，已经提出了许多用于卫星图像的分类方法，特别是基于光谱空间特征提取技术的框架。在本文中，考虑了多光谱数据的特征提取策略，以便通过结合扩展多属性配置文件（EMAP）和稀疏自动编码器（SAE）开发新的分类框架。使用扩展多属性配置文件提取空间信息，然后将其与原始光谱信息结合以描述多光谱图像的光谱空间特性。将获得的特征作为输入送入稀疏自动编码器。最后，将学习到的光谱空间特征嵌入支持向量机（SVM）中进行分类。实验是在两个多光谱（MS）图像上进行的，例如我们构造了相应图像的地面真相图。我们基于EMAP和深度学习（DL）的方法证明了其在合理的运行时间内实现较高分类精度的巨大潜力，并且优于传统分类器和其他分类方法。

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