A Two-Stage Approach Using Genetic Algorithm and Genetic Programming for Remote Sensing Crop Classification

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

@InProceedings{liang:2024:CEC,

• author = "Jing Liang and Zexuan Yang and Tuo Zhang and Ying Bi",
• title = "A Two-Stage Approach Using Genetic Algorithm and Genetic Programming for Remote Sensing Crop Classification",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Support vector machines, Precision agriculture, Crops, Training data, Evolutionary computation, Feature extraction, Genetic Algorithm, Feature Construction, Feature Selection, Crop Classification",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10612210",
• abstract = "Crop classification is an important task in remote sensing image analysis. To effectively classify crops, it is necessary to extract or obtain a set of effective features from raw pixels. However, existing methods have several limitations, including poor interpretability of the learnt models and the requirements of sufficient training data and domain expertise. To address this, this paper develops a two-stage approach using Genetic Algorithm (GA) and Genetic Programming (GP) to automatically learn a feature set that can effectively classify crops using remote sensing images. In the first stage of the new approach, a GP method is applied to automatically construct a set of high-level features by evolving tree-based solutions. In the second stage, a GA method is employed to select a small subset of features from the constructed features and the original features by removing redundant ones. The performance of the new approach is evaluated on three datasets in two scenarios, i.e., classifying four main crop types and all crop types, respectively. The results demonstrate that the new approach achieves more accurate crop classification compared with eight competitive methods.",
• notes = "also known as \cite{10612210}

  WCCI 2024",

}

Genetic Programming entries for Jing Liang Zexuan Yang Tuo Zhang Ying Bi

Citations