A Two-Step Ensemble-based Genetic Algorithm for Land Cover Classification

Accurate land use and land cover (LULC) maps are effective tools to help achieve sound urban planning and precision agriculture. As an intelligent optimization technology, genetic algorithm (GA) has been successfully applied to various image classification tasks in recent years. However, simple GA f...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 16; pp. 1 - 9
Main Authors: Cao, Yang, Feng, Wei, Quan, Yinghui, Bao, Wenxing, Dauphin, Gabriel, Song, Yijia, Ren, Aifeng, Xing, Mengdao
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Classification
Classification algorithms
Clustering algorithms
Clusters
Computation
Convergence
Engineering Sciences
Environmental Sciences
Evolutionary computation
Gene mapping
genetic algorithm
Genetic algorithm (GA)
Genetic algorithms
Genetics
Global Changes
Image classification
Immunity
Land cover
Land use
land use and land cover
land use and land cover (LULC)
Methods
neighborhood window
Neighborhoods
Objective function
Optimization
Precision agriculture
Probability theory
remote sensing image classification
Signal and Image processing
Task analysis
Task complexity
Two-step ensemble
Urban agriculture
Urban planning
ISSN:1939-1404, 2151-1535
Online Access:Get full text
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Summary:Accurate land use and land cover (LULC) maps are effective tools to help achieve sound urban planning and precision agriculture. As an intelligent optimization technology, genetic algorithm (GA) has been successfully applied to various image classification tasks in recent years. However, simple GA faces challenges such as complex calculation, poor noise immunity, and slow convergence. This research proposes a two-step ensemble protocol for LULC classification using a grayscale-spatial-based genetic algorithm model. The first ensemble framework uses FCM to classify pixels into those that are difficult to cluster and those that are easy to cluster, which aids in reducing the search space for evolutionary computation. The second ensemble framework uses neighborhood windows as heuristic information to adaptively modify the objective function and mutation probability of the genetic algorithm, which brings valuable benefits to the discrimination and decision of GA. In this study, three research areas in Dangyang, China, are utilized to validate the effectiveness of the proposed method. The experiments show that the proposed method can effectively maintain the image details, restrain noise, and achieve rapid algorithm convergence. Compared with the reference methods, the best overall accuracy obtained by the proposed algorithm is 88.72%.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2022.3225665