Comparative Analysis of Different Image Fusion Methods at the Pixel and Decision Levels on the Accuracy of Land Use and Land Cover Classification

Document Type : Original Article

Authors

Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

ABSTRACT
Introduction: This research examines and evaluates various methods of fusion satellite images to produce high accuracy land use and land cover maps in the Ahvaz region. Considering the importance of accurate land use information in natural resource management, urban planning, and sustainable development, this study aims to analyze different integration methods and investigate their impact on the accuracy of land use classification. In this context, two major levels of data integration are explored: pixel-level integration and decision-level integration. Pixel-level integration involves combining information from multiple images simultaneously at the individual pixel level, which can significantly improve the accuracy and quality of the final images. On the other hand, decision-level integration focuses on combining the results obtained from different classification algorithms.
Materials and Methods: In this research, images from two sensors, Landsat 8 and Sentinel 2, were used. Landsat 8, with a spatial resolution of 30 meters, and Sentinel 2, with a spatial resolution of 10 meters, were specifically chosen for applications related to land use and land cover.In the first step, images were integrated at the pixel level using various integration methods, including Discrete Wavelet Transform (DWT), Spatial Filtering based on Intensity Modulation (SFIM), Gram Schmidt (GS), Multiplicative (MP), Brovey Transform and Principal Component Analysis (PC). Each of these methods was specifically designed to preserve the spectral and spatial characteristics in the integrated images.In the second step, two classification methods, Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM), were employed to create classified images. This choice was made because of the high ability of both methods to differentiate various land use and land cover classes. As a final step, the use of the Dempster-Shafer method as a decision-level integration approach was examined. This method allows for the combination of evidence and information from various sources, facilitating the production of more accurate and reliable results.
Results and discussion: The results of this study indicated that the Support Vector Machine (SVM) method achieved higher accuracy in land use and land cover classification compared to the Maximum Likelihood Classification (MLC) method. The land use classification derived from the SFIM image using MLC, along with those obtained from GS, PC, and Brovey using SVM, were selected for decision-level integration due to their high Kappa coefficient and overall accuracy. The final land use map obtained through Dempster-Shafer integration exhibited an overall accuracy of 98.38% and a Kappa coefficient of 97.67%. These results reflect an improvement of 5 to 7 percent compared to the four land uses utilized. The increase in accuracy in class differentiation signifies success in more precise identification and classification of land use classes. Furthermore, the results revealed that the Dempster-Shafer method provided significant improvements, particularly in distinguishing similar classes such as soil and residential-road, leading to a notable increase in producer accuracy for these classes.Additionally, the examination of confusion matrices showed that the application of the Dempster-Shafer method reduced ambiguity in the classification of various land use classes. This emphasizes that the correct selection of integration and classification methods can directly enhance the accuracy and quality of land use maps.
Conclusion: This research clearly demonstrates that the use of the Dempster-Shafer method as an effective tool in integrating classified data can significantly increase the accuracy of land use maps. Moreover, this study emphasizes that selecting appropriate integration methods at both the pixel and decision levels can positively impact the quality of land use maps. Ultimately, this research serves as a valuable reference for future studies in the field of remote sensing data integration and its applications in natural resource management and urban planning, underscoring the importance of having accurate land use maps.
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References

Alavi Panah, S.K., 2003, Application of Remote Sensing in Geosciences, University of Tehran, Tehran. (In Farsi).
Al Momani, B., McClean, S. & Morrow, P., 2006, Using Dempster–Shafer to Incorporate Knowledge into Satellite Image Classifi-cation, Artificial Intelligence Review, 25, PP. 161-178, DOI 10.1007/ s10462- 007-9027-4.
Amiri, F. & Nateghi, S., 2023, Land Cover Classification of Bushehr Province Using Landsat 8 and MODIS Data Synthesis Images, Water and Soil Management and Modeling, 3(2), PP. 156-143 (In Farsi), https://doi.org/10.22098/mmws.2022.11372.1124.
Arabameri, A.R., Shirani, K. & Rezaie, K., 2017, Land Capability Zonation toward Landslide Occurance Using Dempster-Shafer and ‎Frequency Ratio Models. Journal of Water and Soil Conservation, 24(3), PP. 41-57 (In Farsi), DOI: 10.22069/jwfst.2017.11196.2554.
Basheer, P.I., Prasad, K.P., Gupta, A.D., Pant, B., Vijavan, V.P. & Kapila, D., 2022, Optimal Fusion Technique for Multi-Scale Remote Sensing Images Based on DWT and CNN, In 2022 8th International Conference on Smart Structures and Systems (ICSSS), PP. 1-6, IEEE, https://doi.org/10.1109/ ICSSS54381.2022.9782239.
Belmahdi, F., Lazri, M., Ouallouche, F., Labadi, K., Absi, R. & Ameur, S., 2023, Application of Dempster-Shafer Theory for Optimization of Precipitation Classifica-tion and Estimation Results from Remote Sensing Data Using Machine Learning, Remote Sensing Applications: Society and Environment, 29, P. 100906, https://doi.org/ 10.1016/j.rsase.2022.100906.
Bendjebbour, A., Delignon, Y., Fouque, L., Samson, V. & Pieczynski, W., 2001, Multisensor Image Segmentation Using Dempster-Shafer Fusion in Markov Fields Context, IEEE Transactions on Geoscience and Remote Sensing, 39(8), PP. 1789-1798, https://doi.org/10.1109/36.942557.
Cayuela, L., Golicher, J.D., Rey, J.S. & Benayas, J.R., 2006, Classification of a Complex Landscape Using Dempster–Shafer Theory of Evidence, International Journal of Remote Sensing, 27(10), PP. 1951-1971, https:// doi.org/10.1080/01431160500181788.
ـــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ
[1]. Cayuela
 
Chao, W.A.N.G., Shuai, W.A.N.G., Xiao, C.H.E.N., Junyong, L.I. & Tao, X.I.E., 2023, Object-Level Change Detection of Multi-Sensor Optical Remote Sensing Images Combined with UNet++ and Multi-Level Difference Module, Acta Geodaetica et Cartographica Sinica, 52(2), P. 283, https:// doi.org/10.11947/j.AGCS.2023.20220202.
Chen, Q., Whitbrook, A., Aickelin, U. & Roadknight, C., 2014, Data Classification Using the Dempster–Shafer Method, Journal of Experimental & Theoretical Artificial Intelligence, 26(4), PP. 493-517, https:// doi.org/10.1080/0952813X.2014.886301.
Chenari, S., Salmani, K. & Mirabedini, M., 2018, Landslide Hazard Mapping Using Dempster-Shafer Theory- A Case Study: Ziarat Watershed, Golestan Province, Iran, Journal of Engineering Geology. 11(4), PP. 385-404 (In Farsi).
Chu, H. & Zhu, W., 2008, Fusion of IKONOS Satellite Imagery Using IHS Transform and Local Variation, IEEE Geoscience and Remote Sensing Letters, 5(4), PP. 653-657, https://doi.org/10.1109/LGRS.2008.2002034.
Dempster, A.P., 1967, Upper and Lower Probabilities Induced by a Multi Valued Mapping, The Annal of Mathematical Statistics, 1967(38), PP. 325-339.
Denoeux, T., 2008, A K-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory, Classic Works of the Dempster-Shafer Theory of Belief Functions, PP. 737-760, https://doi.org/ 10.1109/21.376493.
Durgesh, K.S. & Lekha, B., 2010, Ehlers, M., Klonus, S., Johan Åstrand, P. & Rosso, P., 2010, Multi-Sensor Image Fusion for Pansharpening in Remote Sensing, International Journal of Image and Data Fusion, 1(1), PP. 25-45, https://doi.org/ 10.1080/19479830903561985.
Esmaeelzadeh, R., Emamgholizadeh, S. & Bigdeli, B., 2024, Improvement on the Effective Snow Cover Extraction Using Fusion Satellite Images Approach, Journal of the Indian Society of Remote Sensing, PP. 1-14, https://doi.org/10.1007/s12524-024-01828-y.
Fallah, M. & Azadbakht, M., 2021, Fusion of Thermal Infrared and Visible Images Based on Multi-Scale Transform and Sparse Representation, Journal of Geospatial Information Technology, 8(3), PP. 39-59 (In Farsi), http://dx.doi.org/ 10.52547/jgit.8.3.39.
Fazeli Farsani, A., Ghazavi, R. & Farzaneh, M., 2015, Investigation of Land Use Classification Algorithms Using Images Fusion Techniques (Case Study: Beheshtabad SUB-BASIN), RS & GIS for Natural Resources, 6(1), PP. 91-105 (In Farsi).
Feng, T., Ma, H. & Cheng, X., 2021, Land-Cover Classification of high-Resolution Remote Sensing Image Based on Multi-Classifier Fusion and the Improved Dempster–Shafer Evidence Theory, Journal of Applied Remote Sensing, 15(1), PP. 014506-014506, https://doi.org/10.1117/1.JRS.15.014506.
Foucher, S., Germain, M., Boucher, J.M. & Benie, G.B., 2002, Multisource Classification Using ICM and Dempster-Shafer Theory, IEEE Transactions on Instrumentation and Measurement, 51(2), PP. 277-281, https://doi.org/10.1109/19.997824.
Gharbia, R., El Baz, A.H., Hassanien, A.E. & Tolba, M.F., 2014, Remote Sensing Image Fusion Approach Based on Brovey and Wavelets Transforms, Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 (PP. 311-321), Springer International Publishing, DOI: 10.1007/978-3-319-08156-4_31.
Gilliot, J.M., Le Priol, C., Vaudour, E. & Martin, P., 2018, Automatic Extraction of Agricultural Parcels from Remote Sensing Images and the RPG Database with QGIS/OTB, QGIS and Applications in Agriculture and Forest, 2, PP. 77-117, https://doi.org/10.1002/9781119457107.ch3.
Gungor, O. & Shan, J., 2004, Evaluation of Satellite Image Fusion Using Wavelet Transform, Proceedings of 20th Congress ISPRS “Geo-Imagery Bridging Continents” (PP. 12-13).
Hamza, A., Khan, M.A., Ur Rehman, S., Al-Khalidi, M., Alzahrani, A.I., Alalwan, N. & Masood, A., 2024, A Novel Bottleneck Residual and Self-Attention Fusion-Assisted Architecture for Land Use Recognition in Remote Sensing Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, PP. 2995-3009, https://doi.org/10.1109/ JSTARS.2023.3348874.
Huang, C., Davis, L.S. & Townshend, J.R.G., 2002, An Assessment of Support Vector Machines for Land Cover Classification, International Journal of Remote Sensing, 23(4), PP. 725-749, https://doi.org/10.3390/ rs14040972.
Huang, A., Shen, R., Li, Y., Han, H., Di, W. & Hagan, D.F.T., 2022, A Methodology to Generate Integrated Land Cover Data for Land Surface Model by Improving Dempster-Shafer Theory, Remote Sensing, 14(4), P. 972, https://doi.org/10.3390/ rs14040972.
Jinju, J., Santhi, N., Ramar, K. & Bama, B.S., 2019, Spatial Frequency Discrete Wavelet Transform Image Fusion Technique for Remote Sensing Applications, Engineering Science and Technology, an International Journal, 22(3), PP. 715-726, https://doi.org/ 10.1016/j.jestch.2019.01.004.
Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P. & Waske, B., 2016, A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring, Remote Sensing, 8(1), P. 70, https://doi.org/10.3390/rs8010070.
Judah, A. & Hu, B., 2022, An Advanced Data Fusion Method to Improve Wetland Classification Using Multi-Source Remotely Sensed Data, Sensors, 22(22), P. 8942, https://doi.org/10.3390/s22228942.
Kabolizadeh, M., Rangzan, K. & Mohammadi, S., 2018, Application of Fusion in Satellite Images the Landsat-8 and Sentinel-2 in Environmental Monitoring, Journal of RS and GIS for Natural Resources, 9(3), PP. 53-71 (In Farsi).
Kallel, A., Dalla Mura, M., Fakhfakh, S. & Romdhane, N.B., 2023, Physics-Based Fusion of Sentinel-2 and Sentinel-3 for Higher Resolution Vegetation Monitoring, IEEE Transactions on Geoscience and Remote Sensing, 61, PP. 1-17, https://doi.org/10.1109/TGRS.2023.3257219.
Karasiak, N. & Perbet, P., 2018, Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park, QGIS and Applications in Agriculture and Forest, 2, PP. 215-245, https://doi.org/10.1002/9781119457107.ch7.
Kiyani, V., Alizade Shaabani, A. & Nazari Samani, A., 2014, Assessing the Classification Accuracy of LISS-III Sensor Image of IRS-P6 Satellite Using Google Earth'sDatabase to Provide Land Coverage/ Land Use Maps (Case Study: Taleghan Watershed), Scientific- Research Quarterly of Geographical Data (SEPEHR), 23(90), PP. 59-51 (In Farsi), DOI: 10.22131/ sepehr.2014.12167.
Klonus, S. & Ehlers, M., 2007, Image Fusion Using the Ehlers Spectral Characteristics Preservation Algorithm, GIScience & Remote Sensing, 44(2), PP. 93-116, https://doi.org/10.2747/1548-1603.44.2.93.
Lennert, M., Grippa, T., Radoux, J., Bassine, C., Beaumont, B., Defourny, P. & Wolff, E., 2019, Creating Wallonia's New Very High Resolution Land Cover Maps: Combining Grass GIS Obia and OTB Pixel-Based Results, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, PP. 151-157, https://doi.org/10.5194/isprs-archives-XLII-4-W14-151-2019.
Li, C., Liu, L., Wang, J., Zhao, C., Wang, R. 2004. September, Comparison of two methods of the fusion of remote sensing images with fidelity of spectral information. In IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium. 4, 2561-2564, https://doi.org/10.1109/IGARSS.2004. 1369819.
Liu, X., Liu, Q. & Wang, Y., 2020, Remote Sensing Image Fusion Based on Two-Stream Fusion Network, Information Fusion, 55, PP. 1-15, https://doi.org/ 10.48550/arXiv.1711.02549.
López-Caloca, A.A., 2014, Data Fusion Approach for Employing Multiple Classifiers to Improve Lake Shoreline Analysis, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 19th Iberoamerican Congress, CIARP 2014, Puerto Vallarta, Mexico, November 2-5, 2014, Proceedings 19 (PP. 1022-1029), Springer International Publishing, http://dx.doi.org/ 10.1007/978-3-319-12568-8_124.
Luo, H., Liu, C., Wu, C. & Guo, X., 2018, Urban Change Detection Based on Dempster–Shafer Theory for Multitemporal Very High-Resolution Imagery, Remote Sensing, 10(7), P. 980, https://doi.org/ 10.3390/rs10070980.
Mohammadi Sabet, N., Karami, J. & Sharifikia, M., 2019, Explanation of Uncertainties in Environmental Hazard Zonation Using Dempster-Shafer Theory. Case Study: Landslide Hazard in Southern Chalous, MJSP., 23(1) PP. 1-24, URL: http://hsmsp. modares.ac.ir/article-21-20577-fa.html.
Nitze, I., Schulthess, U. & Asche, H., 2012, Comparison of Machine Learning Algorithms Random Forest, Artificial Neural Network and Support Vector Machine to Maximum Likelihood for Supervised Crop Type Classification, Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil, 79, P. 3540.
Oommen, T., Misra, D., Twarakavi, N.K., Prakash, A., Sahoo, B. & Bandopadhyay, S., 2008, An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing, Mathematical Geosciences, 40, PP. 409-424, http://dx.doi.org/10.1007/s11004-008-9156-6.
Pedroni, L., 2003, Improved Classification of Landsat Thematic Mapper Data Using Modifiedprior Probabilities in Large and Complex Landscapes, International Journal of RemoteSensing, 24, PP. 91-113, https://doi.org/10.1080/01431160304998.
Pohl, C. & Van Genderen, J., 2016, Remote Sensing Image Fusion: A Practical Guide, Crc Press, https://doi.org/10.1201/9781315370101.
Ran, Y.H., Li, X., Lu, L. & Li, Z.Y., 2012, Large-Scale Land Cover Mapping with the Integration of Multi-Source Information Based on the Dempster–Shafer Theory, International Journal of Geographical Information Science, 26(1), PP. 169-191, https://doi.org/10.1080/13658816.2011.577745.
Rashidi, F., Oladi, J. & Babaie Kafaki, S., 2008, Investigation on Satellite Data Fusion Operation Effects on Accuracy of Forest Type Classification (Case Study: Mazandaran Province), Iranian Journal of Forest and Poplar Research, 16(1), PP. 22-11 (In Farsi).
Rasti, B. & Ghamisi, P., 2020, Remote Sensing Image Classification Using Subspace Sensor Fusion, Information Fusion, 64, PP. 121-130, https://doi.org/10.1016/j.inffus. 2020.07.002.
Richa, K. & Singh, P., 2022, An Effective Algorithm of Remote Sensing Image Fusion Based on Discrete Wavelet Transform, Mobile Radio Communications and 5G Networks: Proceedings of Second MRCN 2021 (PP. 313-330), Singapore: Springer Nature Singapore, http://dx.doi.org/ 10.1007/978-981-16-7018-3_24.
Rowshanzamir, S., 2022, Investigation of Effective Factors and Landslide Susceptibility Zonation Using the Dempster-Shafer Model (Case Study: Middle Mazlaghan Chai), Watershed Management Research (In Farsi), DOI: 10.22092/wmrj.2023.360607.1502.
Roy, S.K., Deria, A., Hong, D., Rasti, B., Plaza, A. & Chanussot, J., 2023, Multimodal Fusion Transformer for Remote Sensing Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 61, PP. 1-20, https://doi.org/10.1109/TGRS.2023.3286826.
Saeidi, V., Pradhan, B., Idrees, M.O. & Abd Latif, Z., 2014, Fusion of Airborne LiDAR with Multispectral SPOT 5 Image for Enhancement of Feature Extraction Using Dempster–Shafer Theory, IEEE Transactions on Geoscience and Remote Sensing, 52(10), PP. 6017-6025, https://doi.org/10.1109/TGRS.2013.2294398.
Saghazadeh, N., Ghasemieh, H., Omidvar, E. & Maghsoudi, Y., 2023, Evaluating the Efficiency of Bivariate Models in Determining Subsidence Susceptibility of Kashan Plain Aquifer, Journal of Geography and Environmental Hazard, 11(4), PP. 69-98 (In Farsi), DOI: 10.22067/ geoeh.2022.75050.1168.
Shafer, G., 1976, A Mathematical Theory of Evidence, Princeton University Press, NJ, 1976.
Shahhoseini, R., Azizi, K., Zarei, A. & Moradi, F., 2022, Object-Oriented Classification of Urban Areas Using a Combination of Sentinel-1 and Sentinel-2 Images, Iranian Journal of Remote Sensing & GIS, 14(3), PP. 105-121 (In Farsi), https://doi.org/10.52547/ gisj.14.3.105.
Sun, Y., Zhi, X., Jiang, S., Fan, G., Yan, X. & Zhang, W., 2024, Image Fusion for the Novelty Rotating Synthetic Aperture System Based on Vision Transformer, Information Fusion, 104, P. 102163, https://doi.org/10.1016/j.inffus.2023.102163.
Tupin, F., Inglada, J. & Mercier, G., 2014, Image Processing Techniques for Remote Sensing, Remote Sensing Imagery, PP. 123-154, https://doi.org/10.1002/9781118899106.ch5.
Verde, N.M.-S., 2018, Assessment of Radiometric Resolution Impact on Remote Sensing Data Classiication Accuracy, Remote Sensing, 10(8), P. 1267, https://doi.org/10.3390/rs10081267.
Vogel, A., Seeger, K., Brill, D., Brückner, H., Soe, K.K., Oo, N.W., Kraas, F., 2022, Identifying Land-Use Related Potential Disaster Risk Drivers in the Ayeyarwady Delta (Myanmar) during the Last 50 Years (1974–2021) Using a Hybrid Ensemble Learning Model, Remote Sensing, 14(15), P. 3568, https://doi.org/ 10.3390/rs14153568.
Vorovencii, I., 2023, Comparing the Performance of Different Classification Algorithms for Mapping and Assessing Land Cover Changes in Areas with Surface Mining and Complex Landscape Using Landsat Imagery, Available at SSRN, 4329711, https://dx.doi.org/10.2139/ ssrn.4329711.
Wang, C., Sun, W., Fan, D., Liu, X. & Zhang, Z., 2021, Adaptive Feature Weighted Fusion Nested U-Net with Discrete Wavelet Transform for Change Detection of High-Resolution Remote Sensing Images, Remote Sensing, 13(24), P. 4971, https://doi.org/ 10.3390/rs13244971.
Yang, F., Wei, H. & Feng, P., 2020, A Hierarchical Dempster-Shafer Evidence Combination Framework for Urban Area Land Cover Classification, Measurement, 151, P. 105916, https://doi.org/10.1016/ j.measurement.2018.09.058.
Yang, J., Cheng, G. & Shen, M., 2021, Secure Fusion of Encrypted Remote Sensing Images Based on Brovey, Science China Information Sciences, 64(2), PP. 1-3, https://doi.org/10.1007/s11432-018-9572-x.
Zhang, Y., 2004, Understanding Image Fusion, Photogrammetric Engineering & Remote Sensing, 70(6), PP. 657-661.
Zhang, J., 2010, Multi-Source Remote Sensing Data Fusion: Status and Trends, International Journal of Image and Data Fusion, 1(1), PP. 5-24, https://doi.org/ 10.1080/19479830903561035.
Zhu, H., Basir, O. & Karray, F., 2002, October, Data Fusion for Pattern Classification via the Dempster-Shafer Evidence Theory, IEEE International Conference on Systems, Man and Cybernetics, 7, P. 2, IEEE. https://doi.org/10.1109/ICSMC.2002.1175676.
Zhu, H., Ma, M., Ma, W., Jiao, L., Hong, S., Shen, J. & Hou, B., 2021, A Spatial-Channel Progressive Fusion ResNet for Remote Sensing Classification, Information Fusion, 70, PP. 72-87, https://doi.org/10.1016/ j.inffus.2020.12.008.

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