Semantic understanding based on multi-feature kernel sparse representation and decision rules for mangrove growth
作者：
Highlights：
• We propose a semantic understanding method combining a multi-feature kernel sparse classifier with a decision rule model for mangrove growth
• The sparse representation classifier takes into account the spatial context relations of the samples and introduces the kernel function, and a multi-feature kernel sparse representation classifier can be constructed to classify cover types of mangrove and its surroundings objects.
• We work on semantic comprehension of mangrove area in line with decision rules and further divide mangrove areas into two categories: excellent growth and poor growth.
摘要
•We propose a semantic understanding method combining a multi-feature kernel sparse classifier with a decision rule model for mangrove growth•The sparse representation classifier takes into account the spatial context relations of the samples and introduces the kernel function, and a multi-feature kernel sparse representation classifier can be constructed to classify cover types of mangrove and its surroundings objects.•We work on semantic comprehension of mangrove area in line with decision rules and further divide mangrove areas into two categories: excellent growth and poor growth.
论文关键词：Feature extraction,Semantic understanding,Multi-feature kernel sparse representation,Decision rule
论文评审过程：Received 27 July 2021, Revised 30 October 2021, Accepted 3 November 2021, Available online 20 November 2021, Version of Record 20 November 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102813
