The Fusing of Satellite Images and Using Particle Swarm Optimization Algorithm to Improving Evaluation of Water Body, Focusing on Monitoring and Identifying Flood

Document Type : Research Paper


Department of RS/GIS, Faculty of Geography, University of Tehran, Tehran, Iran.


Iran is one of the countries that, due to its geographical location, is facing a lot of natural disasters that affect many countries and causes a lot of economic and human losses every year. In recent years, Iran has significantly exposed to floods. Because human activity has concentrated in flood-prone areas, which are often the right places to live and economic activity, there is a probability of being a lot of damage. It has caused financial and human losses. Reports of relief and crisis response from the United Nations inferred that floods should be considered one of the most severe natural disasters. The goal of the prevention of floods damage is to improve the quality of life by reducing human and public losses, both economic and environmental.
Flood management actions can have divided into two groups: structural actions and management actions. Structural actions include the physical activities [1] for buildings and facilities to deal with floods, such as actions improving the route of the river, construction of reservoir dams, and longitudinal coastal embankments. These actions are the hardest part of dealing with floods. Management actions include a variety of precautionary actions to reduce flood damage, including land-use control and warning systems of the flood. Such actions constitute the Software aspect of confrontation in floods. These actions should have taken in three areas: flood prevention, response and reconstruction, and improvement of the damaged regions. As mentioned earlier, one of the management actions is flood warning systems to estimate the damages. In this research, have been tried a cost-effective solution to identify and evaluate and damage estimates floods created and provided to using in Flood warning systems.
Methodology and Implication
Part of the Caspian Sea has selected as a suitable study area due to the presence of pure water bodies. Images of the Landsat 8 satellite, the OLI sensor, have been used as the data source to prevent the impact of various sensors. All images selected are cloudless to reduce cloud impact. To minimize time processing, a clipping of images has considered. Some of the images were to validating this purpose method. The resolution of Landsat images (30 m) is vast for identifying small pieces with mixed pixels. For the increasing spatial resolution of images, the IHS image fusion algorithm has used with the panchromatic image.
Due to the spectral behavior of water in different bands, NIR, SWIR, and Green bands were recognized and used. March 2019 has considered due to the floods around the Caspian Sea. The study area was selected part of the Caspian Sea border, around Kiashahr near Lahijan. In the first step, to improve the accuracy of the final results, the three selective bands were combined with a panchromatic band that has twice resolution (15 m) of the above bands.
In the next step, small areas in the deeper part of the sea that do not have cloud cover were used as the standard reflectance of water and to calculate the degree of classification error. The vector angle values of the band and the water reflectance standard value its (such as SAM method) and the distance their values were used to create the map. Probability water in each pixel, its reflectance proximity to the standard reflectance of water in the same band, will be between zero and one.
After creating a probabilistic map of the existence of water, this map enters the optimization algorithm as a relatively simple classification. According to the goal of implementing an optimization algorithm that is detecting and extracting the water range from images, creating a map of the probability of water can be an excellent initial solution for better implementation of the algorithm. In the optimization algorithm, before the implementation of such algorithms, the objective function should be defined, and it used to the optimizing problem.
When its value is more valuable in this problem, that is Larger value. In this research, a means of maximized value is more probability of water. Function and particle swarm algorithm coefficients have determined from the beginning of the algorithm implementation. c1, c2, φ1, φ2, and w, in the PSO algorithm structure, and k1, k2, and k3 in the objective function are coefficients whose values are determined. In the following, Relationship 1 is The function of calculating the probability map of water, Relationship 2 is the objective function [2], Relationships 3, and 4 are a function of the particle swarm algorithm [4,5].
At each stage of implementation, the status of pixels was compared with the best solution of the objective function, if it is better than the best solution up to replace. In addition to each pixel, It will have saved the objective function calculated for the whole range. If the response was better than the optimal state of the global solution, it replaced. In this way, the answers have compared with the most optimal solution Due to defining conditions for the algorithm. Finally, after 500 repetitions, the algorithm ends. Figure 1 is a visual comparison of the proposed method and methods of SVM and k-means in the study area.
By studying and checking the optimization algorithms, the particle swarm algorithm as a collective intelligence algorithm that takes effects of the neighborhood [5], According to the water behavior and The process of creating floods, will be advantageous. This algorithm was selected using an objective function that would cover the essential issues and considering the water probability in the points and the impact of the neighbors. To improving the usability research optimization algorithm, a relatively right initial solution was created by the probabilistic maps of the presence of water in the pixels and using spectral behavior of water and spectral reflection in the used bands.
Finally, the performance of the proposed algorithm was visually and statistically compared with several other classification methods such as SVM and k-means. The Overall Accuracy and Kappa Coefficient values calculated and compared for statistical comparison. The OA value of 98.93% for the proposed algorithm, 98.39% for SVM and 96.73% for k-means, and KC 95.6%, 91.2% and 67.8% to the proposed research algorithm, SVM and k-means. As a result, The proposed algorithm found to be useful and appropriate in this problem. Figure 2 is a statistical comparison chart of the proposed method and methods of SVM and k-means.
Future Work
For future research, other techniques can be used on fusing images and compared with the used method. On the other, using radar images causes increasing accuracy and eliminating cloud effects. Using Modis satellite imagery due to its wide range of spectrum can better distinguish the components of pixels. The using meteorological satellite images to improve the time series of studies, and the quickly monitoring and predicting floods can have a good effect. And using different methods of optimization and comparison with the proposed method in this research to improve the identification and monitoring and pathology of crises such as floods, can be beneficial. Using the time series of images will also be very appropriate and efficient.


Abdullahi, M. (2013). Crisis Management in Areas, Organization of Municipalities and Government Departments. Tehran. Iran.
Asgari, A. et al. (2013). GIS Application in Crisis Management, Organization of municipalities and villages of the country. Tehran. Iran.
Bahrami, N., Argany, M., Neysani Samani, N., Vafaeinejad, A. R. (2019). Designing a context-aware recommender system in the optimization of the relief and rescue, The   ISPRS   international   Geospatial   Conference Joint SMPR and GIResearch (Scopus).
Barnes, B.B., Garcia, R., Hu, H., Lee, Z. (2018). Multi-band spectral matching inversion algorithm to derive water column properties in optically shallow waters: An optimization of parameterization, Remote Sensing of Environment, Volume 204, January 2018, Pages 424-438.
Copin, B. (2012). Book: Artificial Intelligence, Davarpanah, S.H., Mirzaee, A.R., Tehran.
D'Addabbo, A., Refice, A., Lovergine, F. P., Pasquariello, G. (2018). DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping, Computers & Geosciences, Volume 112, March 2018, Pages 64-75.
Fatehian, S., Jelokhani-Niaraki, ., AbdollahiKakroodi, A., YazanpanahDero, Q., Najmeh NeysaniSamany, N. (2018). A volunteered geographic information system for managing environmental pollution of coastal zones: A case study in Nowshahr, Iran, Ocean & Coastal Management, Volume 163, 1 September 2018, Pages 54-65.
Ghavami, Z., Arefi, H., Bigdeli, B., Janalipour, M. (2017). Comprehensive investigation on non-parametric classification methods in order to separate urban objects using the integration of very high spatial resolution LiDAR and aerial data, Jgit, 5 (3), 77-97.
Hosseini, M. (2008). Crisis Management, City INS, Tehran, Iran.
Jeihouni, M., Kakroodi, A.A., Hamzeh, S. (2019). Monitoring shallow coastal environment using Landsat/altimetry data under rapid sea-level change, Estuarine, Coastal and Shelf Science, Volume 224, 31 August 2019, Pages 260-271.
Jia, K., Jiang, W., Li, J., HongTang, Z. (2018). Spectral matching based on discrete particle swarm optimization: A new method for terrestrial water body extraction using multi-temporal Landsat 8 images, Remote Sensing of Environment, Volume 209, May 2018, Pages 1-18.
Karamouz, M., Ahmadi, A., Falahi, M. (2014). System Engineering, University of Amirkabir Press, Tehran, Iran.
RehamGharbia, R., EllaHassanien, A., El-Baz, A. H., Elhoseny, M., Gunasekaran, M. (2018). Multi-spectral and panchromatic image fusion approach using stationary wavelet transform and swarm flower pollination optimization for remote sensing applications, Future Generation Computer Systems, Volume 88, November 2018, Pages 501-511.
Samadzadegan, F., Mohammadi, F. T., Bigdeli, B. (2015). Data Fusion, Univversity of Tehran Press, 2nd Edition.
Samadzadegan, F., Alizadeh, A. (2011). Computational Swarm Intelligence Theory & Application, University of Tehran Press, Tehran, Iran.
Salehian, S., Arefi, H., Shah Hosseini, R. (2019). Change Detection in Urban Area Using Decision Level Fusion of Change Maps Extracted from Optic and SAR Images, JGST, 8 (4), 71-90.
Shaad, K., Burlando, P. (2019). Monitoring and modeling of shallow groundwater dynamics in urban context: The case study of Jakarta, Journal of Hydrology, Volume 573, June 2019, Pages 1046-1056.
Shah-Hosseini, R., Safari, A. R., Homayouni, S. (2018). Monitoring and Estimating Flood Damages by Object-Oriented Change Detection of Optical and Radar Earth Observations, JGST, 8 (1), 239-257.
Victor, P., Oncken, O., Sobiesiak, M., Kemter, M., Gonzalez, G., Ziegenhagen, T. (2018). Dynamic triggering of shallow slip on forearc faults constrained by monitoring surface displacement with the IPOC Creepmeter Array, Earth and Planetary Science Letters, Volume 502, 15 November 2018, Pages 57-73.
Wedajo, G. (2017). LiDAR DEM Data for Flood Mapping and Assessment; Opportunities and Challenges: A Review, Journal of Remote Sensing & GIS, Volume 6, Issue 4, 1000211.
Zhang, Y., Foody, G. M., Ling, F., Li, X., Ge, Y., Du, Y., Atkinson, P. M. (2018). Spatial-temporal fraction map fusion with multi-scale remotely sensed images, Remote Sensing of Environment, Volume 213, August 2018, Pages 162-181.