.png){kind=icon}
返回
Hyperspectral Feature Extraction Using Total Variation Component Analysis
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2016.2593463.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a novel feature extraction method, called orthogonal total variation component analysis (OTVCA), is proposed for remotely sensed hyperspectral data. The features are extracted by minimizing a total variation (TV) penalized optimization problem. The TV penalty promotes piecewise smoothness of the extracted features which is useful for classification. A cyclic descent algorithm called OTVCA-CD is proposed for solving the minimization problem. In the experiments, OTVCA is applied on a rural hyperspectral image having low spatial resolution and an urban hyperspectral image having high spatial resolution. The features extracted by OTVCA show considerable improvements in terms of classification accuracy compared with features extracted by other state-of-the-art methods.
Keyword:
Classification
cyclic descent (CD)
feature extraction (FE)
hyperspectral image
low-rank model
total variation (TV) component analysis
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:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
Configurational assignment of brassinosteroid sidechain by exciton coupled circular dichroic spectroscopy.png)
Tetrahedron
OAAI
TetrahedronIF0
OAAI分享收藏Determination of cross-linking reagent, divinylbenzene in polystyrene-type ion exchange resin precursors with chloromethyl substituents by pyrolysis-gas chromatography in aiding preliminary reduction of chlorine atoms in the samplesPolymer
PolymerIF0
PREAI
分享收藏Acyclic 1,2-/1,3-mixed pentols. Synthesis and general trends in bichromophoric exciton coupled circular dichroic spectraTetrahedronOAAI
TetrahedronIF0
OAAI分享收藏A linear constrained distance-based discriminant analysis for hyperspectral image classificationPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Generalized Graph-Based Fusion of Hyperspectral and LiDAR Data Using Morphological Features基于形态学特征的高光谱与激光雷达数据的广义图融合
PREAI
分享收藏
查看更多内容