首页|Optimizing Spatial Pattern Analysis in Serial Remote Sensing Images through Empirical Mode Decomposition and Ant Colony Optimization

Optimizing Spatial Pattern Analysis in Serial Remote Sensing Images through Empirical Mode Decomposition and Ant Colony Optimization

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  • 国家科技期刊平台
  • NETL
  • NSTL
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Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over time.Although the original images may provide limited insights,they harbor considerable potential for identifying clusters and patterns.The aggregation of these serial remote sensing images(SRSI)becomes increasingly viable as distinct patterns emerge in diverse scenarios,such as suburbanization,the expansion of native flora,and agricultural activities.In a novel approach,we propose an innovative method for extracting sequential patterns by combining Ant Colony Optimization(ACD)and Empirical Mode Decomposition(EMD).This integration of the newly developed EMD and ACO techniques proves remarkably effective in identifying the most significant characteristic features within serial remote sensing images,guided by specific criteria.Our findings highlight a substantial improvement in the efficiency of sequential pattern mining through the application of this unique hybrid method,seamlessly integrating EMD and ACO for feature selection.This study exposes the potential of our innovative methodology,particularly in the realms of urbanization,native vegetation expansion,and agricultural activities.

spatial pattern analysisEMDACO

J Srinivasan、S Uma、Saleem Raja Abdul Samad、Jayabrabu Ramakrishnan

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Department of Computer Applications,Madanapalle Institute of Technology&Science(MITS),Madanapalle 517325,Andhra Pradesh,India

Department of Information Technology,Panimalar Engineering College,Chennai 600123,India

Information Technology Department,College of Computing and Information Sciences,University of Technology and Applied Sciences-Shinas 324,Sultanate of Oman

Department of Information Technology and Security,College of Computer Science and Information Technology,Jazan University,Jazan 45142,Kingdom of Saudi Arabia

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2024

10.11916/j.issn.1005-9113.2023072

哈尔滨工业大学学报(英文版)
哈尔滨工业大学

哈尔滨工业大学学报(英文版)

影响因子：0.238
ISSN：1005-9113
年,卷(期)：2024.31(4)