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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Vydáno v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 16; s. 1 - 9
Hlavní autoři:	Cao, Yang, Feng, Wei, Quan, Yinghui, Bao, Wenxing, Dauphin, Gabriel, Song, Yijia, Ren, Aifeng, Xing, Mengdao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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%.
AbstractList	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 GA model. The first ensemble framework uses fuzzy c-means 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 GA, 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%. 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%.
Author	Dauphin, Gabriel Quan, Yinghui Bao, Wenxing Cao, Yang Feng, Wei Song, Yijia Xing, Mengdao Ren, Aifeng
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SubjectTerms	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
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Title	A Two-Step Ensemble-based Genetic Algorithm for Land Cover Classification
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