Evaluating the Accuracy of Fused OLI Images for Land Cover Classification: A Case Study of Al-Kut City
DOI:
https://doi.org/10.31185/wjfh.Vol21.Iss4.1230Keywords:
data fusion, gram Schmitt, region of interestAbstract
Data fusion is a key technique in remote sensing, enabling the integration of multisensory data to produce more accurate and comprehensive representations of the Earth’s surface. Pan-sharpening, particularly the Gram–Schmidt (GS) method, enhances the spatial resolution of multispectral imagery while maintaining spectral fidelity, making it well-suited for Landsat 8 OLI data. This study evaluates the effectiveness of GS pan-sharpening in improving land-cover classification for Kut city, Iraq. Maximum Likelihood Classification (MLC) was adopted as the primary supervised approach, with regions of interest (ROIs) carefully selected and refined through post-classification analysis. Accuracy assessment using confusion matrices showed overall accuracies of 89.35% for 30 m imagery and 78.99% for 15 m imagery, supported by high Kappa coefficients. The results highlight the advantages of integrating GS pan-sharpening with MLC, offering a reliable framework for land-cover mapping and supporting applications in agriculture, environmental monitoring, and urban planning.
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References
Al-Shammari, R. J., & Na'im Mohsin, A. J. W. J. o. E. S. (2024). Visual Sustainability in Urban Renewal Projects Traditional kut City Center as a Case Study. 12(1).
Amindin, A., Pourghasemi, H. R., Safaeian, R., Rahmanian, S., Tiefenbacher, J. P., Naimi, B. J. R. E., & Management. (2024). Predicting current and future habitat suitability of an endemic species using data-fusion approach: responses to climate change. 94, 149-162.
Badapalli, P. K., Nakkala, A. B., Gugulothu, S., Kottala, R. B. J. E. S., & Environment. (2025). Dynamic land degradation assessment: Integrating machine learning with Landsat 8 OLI/TIRS for enhanced spectral, terrain, and land cover indices. 9(1), 315-335.
Balabanov, O., & Grigori, L. J. S. J. o. S. C. (2025). Randomized block Gram–Schmidt process for the solution of linear systems and eigenvalue problems. 47(1), A553-A585.
Berlin, B. (2024). Ethnobiological classification. In Cognition and categorization (pp. 9-26): Routledge.
Cheong, S.-W., & Huang, F.-T. J. n. Q. M. (2025). Altermagnetism classification. 10(1), 38.
Duan, J., Xiong, J., Li, Y., & Ding, W. J. I. F. (2024). Deep learning based multimodal biomedical data fusion: An overview and comparative review. 102536.
Elshewy, M. A., Mohamed, M. H., & Refaat, M. J. J. o. t. I. S. o. R. S. (2024). Developing a soil salinity model from landsat 8 satellite bands based on advanced machine learning algorithms. 52(3), 617-632.
Eon, R., Wenny, B. N., Poole, E., Eftekharzadeh Kay, S., Montanaro, M., Gerace, A., & Thome, K. J. J. R. S. (2024). Landsat 9 thermal Infrared sensor-2 (TIRS-2) pre-and post-launch spatial response performance. 16(6), 1065.
Harshaw, C., Sävje, F., Spielman, D. A., & Zhang, P. J. J. o. t. A. S. A. (2024). Balancing covariates in randomized experiments with the gram–schmidt walk design. 119(548), 2934-2946.
Hassani, S., Dackermann, U., Mousavi, M., & Li, J. J. I. F. (2024). A systematic review of data fusion techniques for optimized structural health monitoring. 103, 102136.
Holmes, T. R., Poulter, B., McCorkel, J., Jennings, D., Wu, D., Efremova, B., . . . Hook, S. J. J. o. G. R. B. (2024). On‐Orbit Spatial Performance Characterization for Thermal Infrared Imagers of Landsat 7, 8, and 9, ECOSTRESS and CTI. 129(2), e2023JG007506.
Jain, V. J. I. J. o. D. M. (2023). An Overview on Data Mining and Data Fusion. 3(1), 1-5.
Jia, W., Qin, Y., & Zhao, C. J. F. C. (2024). Rapid detection of adulterated lamb meat using near infrared and electronic nose: A F1-score-MRE data fusion approach. 439, 138123.
Jirout, B. (2024). The Politics of Commercialization and the Near Collapse of the Landsat Civil Remote Sensing Program, 1978–1992. In The Rise of the Commercial Space Industry: Early Space Age to the Present (pp. 147-173): Springer.
John, A., Padinjarathala, A., Doheny, E., Cardiff, B., & John, D. J. I. F. (2023). An evaluation of ECG data fusion algorithms for wearable IoT sensors. 96, 237-251.
Kalikhman, T. P. J. U. R., & Sustainability. (2024). The economic situation and ways to solve the environmental problems in the city of Ust-Kut. 2(2), 203-214.
Khlaif, R. K., & Jaber, H. S. (2024). Evaluation of educational services efficiency of Kut City, Iraq, using GIS-Based (AHP). Paper presented at the AIP Conference Proceedings.
Kibrete, F., Woldemichael, D. E., & Gebremedhen, H. S. J. M. (2024). Multi-Sensor data fusion in intelligent fault diagnosis of rotating machines: A comprehensive review. 114658.
Levy, R., Miller, J. A., Barsi, J. A., Thome, K. J., & Markham, B. L. J. R. S. (2024). Landsat 9 transfer to orbit of pre-launch absolute calibration of Operational Land Imager (OLI). 16(8), 1360.
Liu, Z., Deveci, M., Pamučar, D., & Pedrycz, W. J. I. F. (2024). An effective multi-source data fusion approach based on α-divergence in belief functions theory with applications to air target recognition and fault diagnosis. 110, 102458.
Mahmood, T., Li, J., Saba, T., Rehman, A., Ali, S. J. J. o. N., & Applications, C. (2024). Energy optimized data fusion approach for scalable wireless sensor network using deep learning-based scheme. 224, 103841.
Mamun, M., Hasan, M., & An, K.-G. J. E. I. (2024). Advancing reservoirs water quality parameters estimation using Sentinel-2 and Landsat-8 satellite data with machine learning approaches. 81, 102608.
Mandala, M., Hakim, F. L., Indarto, I., Kurnianto, F. A. J. E. R., Engineering, & Management. (2024). Land Use and Land Cover Change in East Java Indonesia from 1972 to 2021: Learning from Landsat. 80(3), 57-69.
Mustaquim, S. J. W. J. o. A. R., & Reviews. (2024). Utilizing remote sensing data and ArcGIS for advanced computational analysis in land surface temperature modeling and land use property characterization. 21(1), 1496-1507.
Ounoughi, C., & Yahia, S. B. J. I. F. (2023). Data fusion for ITS: A systematic literature review. 89, 267-291.
Salman, H. M., Hamdan, H. F., Khalid, R., Al-Kikani, S. J. F. P., & Applications. (2023). Physical Activity Monitoring for Older Adults through IoT and Wearable Devices: Leveraging Data Fusion Techniques. 11(2).
Samatova, G. J. Э. и. с. (2024). DATA ACQUISTION THROUGH THE LANDSAT SATELLITE. (8 (123)), 196-201.
Segreto, T., & Teti, R. J. P. E. (2023). Data quality evaluation for smart multi-sensor process monitoring using data fusion and machine learning algorithms. 17(2), 197-210.
Singh, G., Dahiya, N., Sood, V., Singh, S., Sharma, A. J. E. M., & Assessment. (2024). ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012–2023 with Landsat series data. 196(3), 233.
Skakun, R., Castilla, G., & Jain, P. J. I. J. o. W. F. (2024). Mapping wildfires in Canada with Landsat MSS to extend the National Burned Area Composite (NBAC) time series back to 1972. 33(12).
Wang, D., Xie, Y., Ma, C., Zhao, Y., Yan, D., Chen, H., . . . Hou, X. J. A. S. (2024). Identification of industrial heat source production areas based on SDGSAT-1 thermal infrared imager. 14(6), 2450.
Williams, J., Ackley, S. F., & Mestas-Nuñez, A. M. J. A. P. (2024). Melt pond detection with Sentinel-2 and Landsat-8 in the Beaufort Gyre using Google Earth Engine.
Wu, H., Liu, Y., Pu, Y., Liu, P., Zhao, W., Guo, X. J. I. J. o. P., & Sensing, R. (2024). National-scale nighttime high-temperature anomalies from Landsat-8 OLI images. 212, 212-229.
Yan, L., & Roy, D. J. R. S. o. E. (2025). Using Landsat 8 and 9 operational land imager (OLI) data to characterize geometric distortion and improve geometric correction of Landsat Multispectral Scanner (MSS) imagery. 321, 114679.
Yarahmadi, M., Thome, K., Wenny, B. N., Czapla-Myers, J., Voskanian, N., Tahersima, M., & Eftekharzadeh, S. J. R. S. (2024). Intercomparison of Landsat operational land imager and terra advanced spaceborne thermal emission and reflection radiometer radiometric calibrations using radiometric calibration network data. 16(2), 400.
Zhao, Y., Li, X., Zhou, C., Peng, H., Zheng, Z., Chen, J., & Ding, W. J. I. f. (2024). A review of cancer data fusion methods based on deep learning. 108, 102361.
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