Dimensionality reduction using deep belief network in big data case study: Hyperspectral image classification

D. M. S. Arsa, G. Jati, Aprinaldi Jasa Mantau, Ito Wasito
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引用次数: 15

Abstract

The high dimensionality in big data need a heavy computation when the analysis needed. This research proposed a dimensionality reduction using deep belief network (DBN). We used hyperspectral images as case study. The hyperspectral image is a high dimensional image. Some researched have been proposed to reduce hyperspectral image dimension such as using LDA and PCA in spectral-spatial hyperspectral image classification. This paper proposed a dimensionality reduction using deep belief network (DBN) for hyperspectral image classification. In proposed framework, we use two DBNs. First DBN used to reduce the dimension of spectral bands and the second DBN used to extract spectral-spatial feature and as classifier. We used Indian Pines data set that consist of 16 classes and we compared DBN and PCA performance. The result indicates that by using DBN as dimensionality reduction method performed better than PCA in hyperspectral image classification.
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基于深度信念网络的大数据降维研究:高光谱图像分类
由于大数据的高维数,在进行分析时需要进行大量的计算。本文提出了一种基于深度信念网络(DBN)的降维方法。我们使用高光谱图像作为案例研究。高光谱图像是一种高维图像。在光谱-空间高光谱图像分类中,提出了一些降低高光谱图像维数的研究方法,如LDA和PCA。提出了一种基于深度信念网络的高光谱图像降维分类方法。在建议的框架中,我们使用两个dbn。第一种DBN用于光谱波段降维,第二种DBN用于提取光谱空间特征并作为分类器。我们使用了包含16个类别的Indian Pines数据集,并比较了DBN和PCA的性能。结果表明,采用DBN作为降维方法对高光谱图像进行分类的效果优于PCA。
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