Hyperspectral Imagery Classification Using Sparse Representations of   Convolutional Neural Network Features

doi:10.3390/rs8020099

CORC > 北京大学 > 地球与空间科学学院

	Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features
	Liang, Heming ; Li, Qi
刊名	REMOTE SENSING
	2016
关键词	deep learning deep features sparse representation remote sensing image classification MORPHOLOGICAL ATTRIBUTE PROFILES HIGH-RESOLUTION IMAGES SPATIAL CLASSIFICATION TRANSFORMATIONS
DOI	10.3390/rs8020099
英文摘要	In recent years, deep learning has been widely studied for remote sensing image analysis. In this paper, we propose a method for remotely-sensed image classification by using sparse representation of deep learning features. Specifically, we use convolutional neural networks (CNN) to extract deep features from high levels of the image data. Deep features provide high level spatial information created by hierarchical structures. Although the deep features may have high dimensionality, they lie in class-dependent sub-spaces or sub-manifolds. We investigate the characteristics of deep features by using a sparse representation classification framework. The experimental results reveal that the proposed method exploits the inherent low-dimensional structure of the deep features to provide better classification results as compared to the results obtained by widely-used feature exploration algorithms, such as the extended morphological attribute profiles (EMAPs) and sparse coding (SC).; SCI(E); EI; ARTICLE; heming126liang@126.com; qi.lee009@gmail.com; 2; 8
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/437903]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Liang, Heming,Li, Qi. Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features[J]. REMOTE SENSING,2016.
APA	Liang, Heming,&Li, Qi.(2016).Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features.REMOTE SENSING.
MLA	Liang, Heming,et al."Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features".REMOTE SENSING (2016).