A Two-Stage Approach Using Genetic Algorithm and Genetic Programming for Remote Sensing Crop Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{liang:2024:CEC,
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author = "Jing Liang and Zexuan Yang and Tuo Zhang and Ying Bi",
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title = "A Two-Stage Approach Using Genetic Algorithm and
Genetic Programming for Remote Sensing Crop
Classification",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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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",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612210",
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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.",
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notes = "also known as \cite{10612210}
WCCI 2024",
- }
Genetic Programming entries for
Jing Liang
Zexuan Yang
Tuo Zhang
Ying Bi
Citations