Joint bilateral filtering and spectral similarity-based sparse representation: A generic framework for effective feature extraction and data classification in hyperspectral imaging
作者：
Highlights：
• A generic framework for feature extraction and hyperspectral image classification.
• Simultaneously address HSI feature extraction and classifier in a systemic manner.
• Extract features via joint bilateral filtering (JBF) and sparse representation (SR).
• Apply JBF with the 1st principal component as the guidance image to preserve edges.
• Spectral similarity-based joint SR classification (SS-JSRC) for better performance.
摘要
•A generic framework for feature extraction and hyperspectral image classification.•Simultaneously address HSI feature extraction and classifier in a systemic manner.•Extract features via joint bilateral filtering (JBF) and sparse representation (SR).•Apply JBF with the 1st principal component as the guidance image to preserve edges.•Spectral similarity-based joint SR classification (SS-JSRC) for better performance.
论文关键词：Hyperspectral imaging,Joint bilateral filtering,Sparse representation,Feature extraction,Data classification
论文评审过程：Received 3 October 2016, Revised 30 July 2017, Accepted 7 October 2017, Available online 10 October 2017, Version of Record 6 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.10.008
