Simulated Annealing (SA) is
used in this work as a global optimization technique applied in discrete search
spaces in order to change the characterization of pixels in a Polarimetric
Synthetic Aperture Radar (PolSAR) image which have been classified with different label than the
surrounding land cover type. Accordingly, Land Cover
type classification is achieved with high reliability. For this purpose, an
energy function is employed which is minimized by means of SA when the false
classified pixels are correctly labeled. All PolSAR pixels are initially
classified using 9 specifically selected types of land cover by means of Google
Earth maps. Each Land Cover Type is represented by a histogram of the 8
Cameron’s elemental scatterers by means of coherent target decomposition (CTD).
Each PolSAR pixel is categorized according to the local histogram of the
elemental scatterers. SA is applied in the discreet space of nine land cover
types. Classification results prove that the Simulated Annealing approach used
is very successful for correctly separating regions with different Land Cover
Types.
References
[1]
Tong, X.Y., Xia, G.S., Lu, Q., Shen, H., Li, S., You, S. and Zhang, L. (2020) Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models. Remote Sensing of Environment, 237, Article ID: 111322.
https://doi.org/10.1016/j.rse.2019.111322
[2]
Tsagaris, V., Anastassopoulos, V. and Lampropoulos, G.A. (2005) Fusion of Hyperspectral Data Using Segmented PCT for Color Representation and Classification. IEEE Transactions on Geoscience and Remote Sensing, 43, 2365-2375.
https://doi.org/10.1109/TGRS.2005.856104
[3]
Gavade, A.B. and Rajpurohit, V.S. (2019) Systematic Analysis of Satellite Image-Based Land Cover Classification Techniques: Literature Review and Challenges. International Journal of Computers and Applications, 43, 514-523.
https://doi.org/10.1080/1206212X.2019.1573946
[4]
Talukdar, S., Singha, P., Mahato, S., Shahfahad Pal, S., Liou, Y-A. and Rahman, A. (2020) Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12, Article No. 1135.
https://doi.org/10.3390/rs12071135
[5]
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y. and Ranagalage, M. (2020) Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing, 12, Article No. 2291. https://doi.org/10.3390/rs12142291
[6]
Pandey, P.C., Koutsias, N., Petropoulos, G.P., Srivastava, P.K. and Ben Dor, E. (2021) Land Use/Land Cover in View of Earth Observation: Data Sources, Input Dimensions, and Classifiers—A Review of the State of the Art. Geocarto International, 36, 957-988. https://doi.org/10.1080/10106049.2019.1629647
[7]
Chughtai, A.H. and Abbasi, H. (2021) A Review on Change Detection Method and Accuracy Assessment for Land Use Land Cover. Remote Sensing Applications: Society and Environment, 22, Article ID: 100482.
https://doi.org/10.1016/j.rsase.2021.100482
[8]
Soni, P.K., Rajpal, N., Mehta, R. and Mishra, V.K. (2021) Urban Land Cover and Land Use Classification Using Multispectral Sentinal-2 Imagery. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-021-10991-0
[9]
Lee, J.S. and Pottier, E. (2009) Polarimetric Radar Radar Imaging. CRC Press. New York.
[10]
Nunziata, F., Migliaccio, M. and Brown, C.E. (2012) Reflection Symmetry for Polarimetric Observation of Man-Made Metallic Targets at Sea. IEEE Journal of Oceanic Engineering, 37, 384-394. https://doi.org/10.1109/JOE.2012.2198931
[11]
Addabbo, P., Biondi, F., Clemente, C., Orlando D. and Pallotta. L. (2019) Classification of Covariance Matrix Eigenvalues in Polarimetric SAR for Environmental Monitoring Applications. IEEE Transactions on Aerospace and Electronic Systems Magazine, 34, 28-43. https://doi.org/10.1109/MAES.2019.2905924
[12]
Pallotta, L., Clemente, C., De Maio, A. and Soraghan, J.J. (2017) Detecting Covariance Symmetries in Polarimetric SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 55, 80-95. https://doi.org/10.1109/TGRS.2016.2595626
[13]
Touzi, R., Charbonneau, F., Hawkins, R., Murnaghan, K. and Kavoun, X. (2001) Ship–sea contrast optimization when using polarimetric SARs. International Geoscience and Remote Sensing Symposium (IGARSS ’01), Vol. 1, Sydney, 9-13 July 2001, 426-428. https://doi.org/10.4095/219781
[14]
Touzi, R., Charbonneau, F., Hawkins, R. and Vachon, P. (2004) Ship Detection and Characterization Using Polarimetric SAR. Canadian Journal of Remote Sensing, 30, 552-559. https://doi.org/10.5589/m04-002
[15]
Ringrose, R. and Harris, N. (1999) Ship Detection Using Polarimetric SAR Data. SAR Workshop: Committee on Earth Observation Satellites (CEOS), Toulouse, 26-29 October 1999, 687-691.
[16]
Cameron, W.L., Youssef, N.N. and Leung, L.K. (1996) Simulated Polarimetric Signatures of Primitive Geometrical Shapes. IEEE Transactions on Geoscience and Remote Sensing, 34, 793-803. https://doi.org/10.1109/36.499784
[17]
Kouroupis, G. and Anastassopoulos, V. (2019) Scatterer Characterization Based on the Condiagonalization of the Sinclair Backscattering Matrix. Progress in Electromagnetics Research M, 85, 59-69. https://doi.org/10.2528/PIERM19010902
[18]
Kouroupis, G. and Anastassopoulos V. (2019) A Polarimetric CFAR ship Detector Based on the Joint Probability Function of Simulated First-Order Markov Chains. International Journal of Remote Sensing, 40, 5121-5140.
https://doi.org/10.1080/01431161.2019.1579379
[19]
Therrien, C.W. (1992) Random Processes. Prentice Hall Signal Processing Series, Prentice Hall, Hoboken.
[20]
Kouroupis, G. and Anastassopoulos, V. (2016) A Markov Chain Model Based on Cameron’s CTD Ship Detection Scheme. IEEE Imaging Systems and Techniques, Chania, 4-6 October 2016, 100-105. https://doi.org/10.1109/IST.2016.7738205
[21]
Koukiou, G. and Anastassopoulos, V. (2021) Fully Polarimetric Land Cover Classification Based on Markov Chains. Advances in Remote Sensing, 10, 47-65.
https://doi.org/10.4236/ars.2021.103003
[22]
Le Hégarat-Mascle, S., Vidal-Madjar, D. and Olivier, P. (1996) Applications of Simulated Annealing to SAR Image Clustering and Classification Problems. International Journal of Remote Sensing, 17, 1761-1776.
https://doi.org/10.1080/01431169608948738
[23]
Bao, M. (1999) Classification of Multi-Temporal SAR Images and INSAR Coherence Images Using Adaptive Neighborhood Model and Simulated Annealing Approach. Proceedings of the 20th Asia Conference on Remote Sensing, Hong Kong (China), 22-25 November 1999, 811-816.
[24]
Yuan, H., Khorram, S. and Dai, X. (1999) Applications of Simulated Annealing Minimization Technique to Unsupervised Classification of Remotely Sensed Data. IEEE 1999 International Geoscience and Remote Sensing Symposium, Hamburg, 28 June-2 July 1999, 134-136.
[25]
Macri Pellizzeri, T., Lombardo, P., Oliver, C.J., Sciotti, M., Meloni, M. and McConnell, I. (2003) A Comparison of Statistical Segmentation Techniques for Polarimetric SAR: Region Growing versus Simulated Annealing. Proceedings of the Workshop on POLinSAR—Applications of SAR Polarimetry and Polarimetric Interferometry (ESA SP-529), Frascati, 14-16 January 2003, 1-6.
[26]
Manikonda, S.V.T., Sheta, A., Katangur, A. and King, S.A. (2018) Metaheuristic Search Algorithms for Oil Spill Detection Using SAR Images. 2018 8th International Conference on Computer Science and Information Technology (CSIT), Amman, 11-12 July 2018, 143-149. https://doi.org/10.1109/CSIT.2018.8486150
[27]
Wang, L., Li, N., Zhang, Xn, Wei, T., Chen, Y.F. and Zha, J.F. (2018) Full Parameters Inversion Model for Mining Subsidence Prediction Using Simulated Annealing Based on Single Line of Sight D-InSAR. Environmental Earth Sciences, 77, Article No. 161. https://doi.org/10.1007/s12665-018-7355-0
[28]
Strzelczyk, S.P., Strzelczyk, J., Szostek, K., Dwornik, M., Lesniak, A., Bala, J. and Franczyk, A. (2022) Information Extraction from Satellite-Based Polarimetric SAR Data Using Simulated Annealing and SIRT Methods and GPU Processing. Energies, 15, Article No. 72. https://doi.org/10.3390/en15010072
[29]
Ertunç, E., Uyan, M. and Tongur, V. (2020) Land Reallocation Model with Simulated Annealing Algorithm. Survey Review, 53, 383-389.
https://doi.org/10.1080/00396265.2020.1780406
[30]
Sarker, S., Veremyev, A., Boginski, V. and Singh, A. (2019) Critical Nodes in River Networks. Scientific Reports, 9, Article No. 11178.
https://doi.org/10.1038/s41598-019-47292-4
https://www.nature.com/articles/s41598-019-47292-4.
[31]
Sarker, S. (2021) Investigating Topologic and Geometric Properties of Synthetic and Natural River Networks under Changing Climate. University of Central Florida, Orlando.
[32]
Sarker, S., Veremyev, A., Boginski, V. and Singh, A. (2019) Spectral Properties of River Networks. AGU Fall Meeting Abstracts 2019, San Francisco, 9-13 December 2019, EP51C-2107.
[33]
Sarker, S. and Singh, A. (2017) On the Topologic Properties of River Networks. AGU Fall Meeting Abstracts 2017, NASA/ADS, December 2017, IN33B-0128.
[34]
Sarker, S. (2022) Essence of MIKE 21C (FDM Numerical Scheme): Application on the River Morphology of Bangladesh. Open Journal of Modeling and Simulation, 10, 88-117. https://doi.org/10.4236/ojmsi.2022.102006
Cameron, W.L. and Rais, H. (2006) Conservative Polarimetric Scatterers and Their Role in Incorrect Extensions of the Cameron Decomposition. IEEE Transactions on Geoscience and Remote Sensing, 44, 3506-3516.
https://doi.org/10.1109/TGRS.2006.879115
