| Related citation: | J Srinivasan,S Uma,Saleem Raja Abdul Samad,Jayabrabu Ramakrishnan.Optimizing Spatial Pattern Analysis in Serial Remote Sensing Images through Empirical Mode Decomposition and Ant Colony Optimization[J].Journal of Harbin Institute Of Technology(New Series),2024,31(4):52-60.DOI:10.11916/j.issn.1005-9113.2023072. |
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| Author Name | Affiliation | | J Srinivasan | Department of Computer Applications, Madanapalle Institute of Technology & Science MITS, Madanapalle 517325, Andhra Pradesh, India | | S Uma | Department of Information Technology, Panimalar Engineering College, Chennai 600123, India | | Saleem Raja Abdul Samad | Information Technology Department, College of Computing and Information Sciences,University of Technology and Applied Sciences-Shinas 324, Sultanate of Oman | | Jayabrabu Ramakrishnan | 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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| Abstract: |
| 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. |
| Key words: spatial pattern analysis EMD ACO |
| DOI：10.11916/j.issn.1005-9113.2023072 |
| Clc Number:TP751 |
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