آمار

تعداد نشریات13
تعداد شماره‌ها237
تعداد مقالات2,406
تعداد مشاهده مقاله3,888,047
تعداد دریافت فایل اصل مقاله2,826,474
Karim Tabbahfar, H., Tabib Mahmoudi, F.. (1402). Optimum Spectral Indices for Water Bodies Recognition Based on Genetic Algorithm and Sentinel-2 Satellite Images. فناوری آموزش, 12(1), 217-226. doi: 10.22061/jecei.2023.10118.678
H. Karim Tabbahfar; F. Tabib Mahmoudi. "Optimum Spectral Indices for Water Bodies Recognition Based on Genetic Algorithm and Sentinel-2 Satellite Images". فناوری آموزش, 12, 1, 1402, 217-226. doi: 10.22061/jecei.2023.10118.678
Karim Tabbahfar, H., Tabib Mahmoudi, F.. (1402). 'Optimum Spectral Indices for Water Bodies Recognition Based on Genetic Algorithm and Sentinel-2 Satellite Images', فناوری آموزش, 12(1), pp. 217-226. doi: 10.22061/jecei.2023.10118.678
Karim Tabbahfar, H., Tabib Mahmoudi, F.. Optimum Spectral Indices for Water Bodies Recognition Based on Genetic Algorithm and Sentinel-2 Satellite Images. فناوری آموزش, 1402; 12(1): 217-226. doi: 10.22061/jecei.2023.10118.678

Optimum Spectral Indices for Water Bodies Recognition Based on Genetic Algorithm and Sentinel-2 Satellite Images

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 14، دوره 12، شماره 1، فروردین 2024، صفحه 217-226    اصل مقاله (846.26 K)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.10118.678
نویسندگان
H. Karim Tabbahfar؛ F. Tabib Mahmoudi*
Geomatics Engineering Department, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
تاریخ دریافت: 16 مرداد 1402،  تاریخ بازنگری: 02 آبان 1402،  تاریخ پذیرش: 14 آبان 1402 
چکیده
Background and Objectives: Considering the drought and global warming, it is very important to monitor changes in water bodies for surface water management and preserve water resources in the natural ecosystem. For this purpose, using the appropriate spectral indices has high capabilities to distinguish surface water bodies from other land covers. This research has a special consideration to the effect of different types of land covers around water bodies. For this reason, two different water bodies, lake and wetland, have been used to evaluate the implementation results.
Methods: The main objective of this research is to evaluate the capabilities of the genetic algorithm in optimum selection of the spectral indices extracted from Sentinel-2 satellite image due to distinguish surface water bodies in two case studies: 1) the pure water behind the Karkheh dam and 2) the Shadegan wetland having water mixed with vegetation. In this regard, the set of optimal indices is obtained with the genetic algorithm followed by the support vector machine (SVM) classifier.
Results: The evaluation of the classification results based on the optimum selected spectral indices showed that the overall accuracy and Kappa coefficient of the recognized surface water bodies are 98.18 and 0.9827 in the Karkheh dam and 98.04 and 0.93 in Shadegan wetland, respectively. Evaluation of each of the spectral indices measured in both study areas was carried out using quantitative decision tree (DT) classifier. The best obtained DT classification results show the improvements in overall accuracy by 1.42% in the Karkheh Dam area and 1.56% in the Shadegan Wetland area based on the optimum selected indices by genetic algorithm followed by SVM classifier. Moreover, the obtained classification results are superior compared with Random Forest classifier using the optimized set of spectral features.
Conclusion: Applying the genetic algorithm on the spectral indices was able to obtain two optimal sets of effective indices that have the highest amount of accuracy in classifying water bodies from other land cover objects in the study areas. Considering the collective performance, genetic algorithm selects an optimal set of indices that can detect water bodies more accurately than any single index.
کلیدواژه‌ها
Genetic Algorithm؛ Spectral Indices؛ Water Bodies؛ Classifier؛ Optimization
مراجع
[1] C. J. Vörösmarty, P. Green, J. Salisbury, R. B. Lammers, "Global water resources: Vulnerability from climate change and population growth," Science, 289(5477): 284-288, 2000.  

[2] A. Karpatne, A. Khandelwal, X. Chen, V. Mithal, J.  Faghmous, V. Kumar, "Global monitoring of inland water dynamics: State-of-the art, challenges, and opportunities," Studies in Computational Intelligence, 645: 121-147, 2016.

[3] L. Niu, H. Kaufmann, G. Xu, G. Zhang, C. Ji, Y. He, M. Sun, "Triangle Water Index (TWI): An advanced approach for more accurate detection and delineation of water surfaces in sentinel-2 data," Remote Sens., 14(21): 5289, 2022.

[4] L. Shen, C. Li, "Water body extraction from Landsat ETM+ imagery using adaboost algorithm," in Proc. 18th International Conference on Geoinformatics: 1-4, 2010.

[5] A. Saber, I. El-Sayed, M. Rabah, M. Selim, "Evaluating change detection techniques using remote sensing data: Case study New Administrative Capital Egypt," Egypt. J. Remote Sens. Space Sci., 24(2021): 635-648, 2021.  

[6] J. Li, R. Ma, Z. Cao, K. Xue, J. Xiong, M. Hu, X. Feng, "Satellite detection of surface water extent: A review of methodology," Water, 14: 1148, 2022.

[7] J. Bhangale, S. More, T. Shaikh, S. Patil, N. More, "Analysis of surface water resources using sentinel-2 imagery," Procedia Comput. Sci. 171: 2645-2654, 2020.

[8] A. Ogilvie, G. Belaud, S. Massuel, M. Mulligan, P. Le Goulven, R. Calvez, "Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series," Hydrol. Earth Syst. Sci., 22: 4349-4380, 2018.

[9] S. K. McFeeters, "The use of normalized difference water index (NDWI) in the delineation of open water features," Int. J. Remote Sens., 17: 1425-1432, 1996.

[10] C. Kwang, E. M. Osei Jnr, A. S. Amoah, "Comparing of landsat 8 and sentinel 2a using water extraction indexes over volta river," J. Geogr. Geol., 10(1): 1-7, 2018.

[11] A. Fisher, N. Flood, T. Danaher, "Comparing Landsat water index methods for automated water classification in eastern Australia," Remote Sens. Environ. 175: 167-182, 2016.

[12] H. W. Khalid, R. M. Zahid Khalil, M. A. Qureshi, "Evaluating spectral indices for water bodies extraction in western Tibetan Plateau," Egypt. J. Remote Sens. Space Sci. 24(2021): 619-634, 2021.

[13] D. Montero, C. Aybar, M. D. Mahecha, F. Martinuzzi, M. Söchting, S. Wieneke, "A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research,"  Sci. Data, 10(197), 2023.

[14] T. D. Acharya, A. Subedi, I. T. Yang, D. H. Lee, "Combining water indices for water and background threshold in landsat image," In Multidisciplinary Digital Publishing Institute Proceedings, 2(3): 143, 2017.

[15] G. Feyisa, H. Meilby, R. Fensholt, S. Proud, "Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery," Remote Sens. Environ., 140: 23-35, 2014.

[16] H. Emami, A. Zarei, "Modeling lake water's surface changes using environmental and remote sensing data: A case study of lake Uremia," Remote Sens. Appl. Soc. Environ., 23: 100594, 2021.

[17] L. Ji, Zhang, B. Wylie, "Analysis of dynamic thresholds for the normalized difference water index," Photogram. Eng. Remote Sens., 75: 1307-1317, 2009.

[18] B. P. Salmon, W. Kleynhans, F. Van Den Bergh, J. Olivier, T. L. Grobler, K. J. Wessels, "Land cover change detection using the internal covariance matrix of the extended Kalman filter over multiple spectral bands," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6(3): 1079-1085, 2013.

[19] M. Volpi, G. P. Petropoulos, M. Kanevski, "Flooding extent cartography with Landsat TM imagery and regularized kernel Fisher's discriminant analysis," Comput. Geosci., 57 : 24-31, 2013.

[20] C. Huang, Y. Chen, S. Zhang, J. Wu, "Detecting, extracting, and monitoring surface water from space using optical sensors: A review," Rev. Geophys., 56: 333-360, 2018.

[21] H. Q. Xu, "Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery," Int. J. Remote Sens., 27(14): 3025-3033, 2006.

[22] A. Fisher, T. A. Danaher, "Water index for spot5 hrg satellite imagery, New South Wales, Australia, Determined By linear discriminant analysis," Remote Sens., 5(11): 5907-5925, 2013.

[23] M. G. Altarabichi, S. Nowaczyk, S. Pashami, P. Sheikholharam Mashhadi, "Fast genetic algorithm for feature selection-A qualitative approximation approach," Expert Syst. Appl., 211: 118528, 2023.

[24] B. Olueye, A. Leisa, J. Leng, D. Dean, "A genetic algorithm-based feature selection," Int. J. Electron. Commun. Comput. Eng., 5(4): 899-905, 2014.

[25] F. Samadzadegan, F. Tabib Mahmoudi, "Optimum band selection in hyperspectral imagery using swarm intelligence optimization algorithms," in Proc. International Conference on Image Information Processing (ICIIP), 2011.  

[26] F Tabib Mahmoudi, "Investigation of water stress status of plants in north of Iran under under the influence of quarantine quarantine application in Covid-19 virus pandemic," J. Water Soil Conserv., 27(6), 2021.

[27] F. Tabib Mahmoudi, "Semantic object-based urban scene analysis for feature fusion of VHR imagery and Lidar DSM" Signal, Image Video Process., 2022.

[28] S. Khoramak, F. Tabib Mahmoudi, "Multi‑agent hyperspectral and lidar features fusion for urban vegetation mapping," Earth Sci. Inf., 2023.

[29] O. Kavats, D. Khramov, K. Sergieieva, "Surface water mapping from SAR images using optimal threshold selection method and reference water mask," Water, 14: 4030, 2022.

[30] N. Nasir, A. Kansal, O. Alshaltone, F. Barneih, A. Shanableh, M. Al-Shabi, A. Al Shammaa, "Deep learning detection of types of water-bodies using optical variables and ensembling," Intell. Syst. Appl., 18: 200222, 2023.

 

آمار
تعداد مشاهده مقاله: 524
تعداد دریافت فایل اصل مقاله: 422


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب