.png){kind=icon}
返回
A sparse tensor-based classification method of hyperspectral image
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2019.107361.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Previous studies have demonstrated that spatial information can provide significant improvement for the accuracy hyperspectral image (HSI) classification. However, it remains a challenging task to extract three-dimensional (3-D) features from HSI directly. In this paper, a sparse tensor-based classification (STC) method for HSI is proposed. Different from the traditional vector-based or matrix-based methods, the STC utilizes tensor technique to extract the joint spatial-spectral tensor features. We exploit the principal component analysis (PCA) and the 3-D intrinsic spatial-spectral tensors of HSI to alleviate within-class spatial-spectral variation, and to improve the classification performance simultaneously. First, the HSI is segmented into a number of overlapping 3-D tensor patches, which are modelled as summation of intrinsic spatial-spectral tensors and corresponding variation tensors in the next step. Second, the intrinsic spatial-spectral tensor is decomposed into three matrices and a core tensor by the Tucker decomposition (TKD). Sparsity constraint is enforced on the core tensor to extract joint sparse spatial-spectral features. We utilize tensor-based dictionary learning algorithm to train three dictionaries, in order to extract more discriminative tensor features for classification. Finally, we use the support vector machine (SVM) to perform the pixel-wise classification. Experimental results on real HSI datasets demonstrate the proposed method can achieve accurate and robust classification results, and can provide competitive results to state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Hyperspectral image classification
Tensor-based dictionary learning
Sparse tensor-based representation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
Hyperspectral dimensionality reduction for biophysical variable statistical retrieval用于生物物理变量统计检索的高光谱降维
PREAI
分享收藏Hyperspectral Image Classification Using Dictionary-Based Sparse Representation基于字典稀疏表示的高光谱图像分类
PREAI
分享收藏EVALUATION OF FLEXIBLE AND INTERACTIVE TRADEOFF METHOD BASED ON NUMERICAL SIMULATION EXPERIMENTS基于数值模拟实验的柔性交互式权衡方法评价
OAAI
OAAI分享收藏Discriminative Low-Rank Gabor Filtering for Spectral-Spatial Hyperspectral Image Classification
PREAI
分享收藏分享收藏
查看更多内容