Optimization of Remote Sensing Image Attributes to Improve Classification Accuracy

Abstract

Remote sensing technologies provide very important big data to various science areas such as risk identification, damage detection and prevention studies. However, the classification processes used to create thematic maps to interpret this data can be ineffective due to the wide range of properties that these images provide. At this point, there arises a requirement to optimize the data. The first objective of this study is to evaluate the performance of the Bat Search Algorithm which has not previously been used for improving the classification accuracy of remotely sensed images by optimizing attributes. The second objective is to compare the performance of the Genetic Algorithm, Bat Search Algorithm, Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm, which are used in many areas of the literature for the optimization of the attributes of remotely sensed images. For these purposes, an image from the Landsat 8 satellite is used. The performance of the algorithms is compared by classifying the image using the K-Means method. The analysis shows a 10-22% increase in overall accuracy with the addition of attribute optimization.

Keywords

• Remote Sensing,Classification,Optimization,Unsupervised Classification

References

1. Acar, I. and S. E. Butt (2016). "Modeling nurse-patient assignments considering patient acuity and travel distance metrics." Journal of Biomedical Informatics 64: 192-206.
2. Akhtar, S., et al. (2012). A metaheuristic bat-inspired algorithm for full body human pose estimation. Computer and Robot Vision (CRV), 2012 Ninth Conference on, IEEE.
3. Allahverdi, A. and F. S. Al-Anzi (2006). "A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application." Computers & Operations Research 33(4): 1056-1080.
4. Baatz, M. (2000). Multi resolution Segmentation: an optimum approach for high quality multi scale image segmentation. Beutrage zum AGIT-Symposium. Salzburg, Heidelberg, 2000.
5. Blumenstein, B., et al. (2018). "A case of sustainable intensification: Stochastic farm budget optimization considering internal economic benefits of biogas production in organic agriculture." Agricultural Systems 159: 78-92.
6. Eberhart, R. C. and Y. Shi (1998). Comparison between genetic algorithms and particle swarm optimization. International conference on evolutionary programming, Springer.
7. Eberhart, R. C. and Y. Shi (2000). Comparing inertia weights and constriction factors in particle swarm optimization. Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, IEEE.
8. Gandomi, A. H., et al. (2013). "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems." Engineering with computers 29(1): 17-35.

9. Holland, J. (1975). "Adaptation in natural and artificial systems: an introductory analysis with application to biology." Control and artificial intelligence.
10. Huang, C.-L. and J.-F. Dun (2008). "A distributed PSO–SVM hybrid system with feature selection and parameter optimization." Applied soft computing 8(4): 1381-1391.
11. Huang, J. S. and J. Song (2018). "Optimal inventory control with sequential online auction in agriculture supply chain: an agent-based simulation optimisation approach." International Journal of Production Research 56(6): 2322-2338.
12. Kennedy, J. (2011). Particle swarm optimization. Encyclopedia of machine learning, Springer: 760-766.
13. Khan, K., et al. (2011). A fuzzy bat clustering method for ergonomic screening of office workplaces. Third International Conference on Software, Services and Semantic Technologies S3T 2011, Springer.
14. Lemma, T. A. and F. B. M. Hashim (2011). Use of fuzzy systems and bat algorithm for exergy modeling in a gas turbine generator. Humanities, Science and Engineering (CHUSER), 2011 IEEE Colloquium on, IEEE.
15. Li, J. H. (2018). "Optimization and Operation Mechanism of Agriculture Products Electricity Supplier Logistics Distribution Based on Supply Chain Strategic Coordination." Journal of Advanced Oxidation Technologies 21(2).
16. Lillesand, T., et al. (2014). Remote sensing and image interpretation, John Wiley & Sons.
17. Lindley, D. V. (1991). "Making decisions."
18. Pal, S. K. and P. P. Wang (2017). Genetic algorithms for pattern recognition, CRC press.
19. Perumal, K., et al. (2011). Test data generation: a hybrid approach using cuckoo and tabu search. International Conference on Swarm, Evolutionary, and Memetic Computing, Springer.
20. Poli, R., et al. (2007). "Particle swarm optimization." Swarm intelligence 1(1): 33-57.
21. Sagir, M. and Z. K. Ozturk (2010). "Exam scheduling: Mathematical modeling and parameter estimation with the Analytic Network Process approach." Mathematical and Computer Modelling 52(5-6): 930-941.
22. Saraç, T. and M. S. Özdemir (2003). A genetic algorithm for 1, 5 dimensional assortment problems with multiple objectives. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer.
23. Shi, Y. and R. C. Eberhart (1998). Parameter selection in particle swarm optimization. International conference on evolutionary programming, Springer.
24. Walton, S., et al. (2011). "Modified cuckoo search: a new gradient free optimisation algorithm." Chaos, Solitons & Fractals 44(9): 710-718.
25. Yang, X.-S. (2010). Nature-inspired metaheuristic algorithms, Luniver press.
26. Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010), Springer: 65-74.
27. Yang, X.-S. and S. Deb (2009). Cuckoo search via Lévy flights. Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, IEEE.
28. Zhang, Q. and E. Izquierdo (2006). "A multi-feature optimization approach to object-based-image classification." Image and Video Retrieval, Proceedings 4071: 310-319.