Genetic Programming for High-Level Feature Learning in Crop Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Lu:2022:RemoteSensing,

• author = "Miao Lu and Ying Bi and Bing Xue and Qiong Hu and Mengjie Zhang and Yanbing Wei and Peng Yang and Wenbin Wu",
• title = "Genetic Programming for High-Level Feature Learning in Crop Classification",
• journal = "Remote Sensing",
• year = "2022",
• volume = "14",
• number = "16",
• pages = "3982",
• month = aug,
• note = "Special Issue Remote Sensing for Mapping Farmland and Agricultural Infrastructure",
• keywords = "genetic algorithms, genetic programming, crop classification, feature learning, high-level features, genetic programming representation",
• publisher = "MDPI AG",
• ISSN = "2072-4292",
• DOI = "doi:10.3390/rs14163982",
• size = "18 page",
• abstract = "Information on crop spatial distribution is essential for agricultural monitoring and food security. Classification with remote-sensing time series images is an effective way to obtain crop distribution maps across time and space. Optimal features are the precondition for crop classification and are critical to the accuracy of crop maps. Although several approaches are available for extracting spectral, temporal, and phenological features for crop identification, these methods depend heavily on domain knowledge and human experiences, adding uncertainty to the final crop classification. This study proposed a novel Genetic Programming (GP) approach to learning high-level features from time series images for crop classification to address this issue. We developed a new representation of GP to extend the GP tree’s width and depth to dynamically generate either fixed or flexible informative features without requiring domain knowledge. This new GP approach was wrapped with four classifiers,",
• notes = "Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China",

}

Genetic Programming entries for Miao Lu Ying Bi Bing Xue Qiong Hu Mengjie Zhang Yanbing Wei Peng Yang Wenbin Wu

Citations