Investigating the impact of Pan Sharpening on the accuracy of land cover mapping in Landsat OLI imagery

    Komeil Rokni Affiliation


Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using a panchromatic image with a higher resolution, to generate a high resolution multispectral image. The present study aims at assessing the power of Pan Sharpening on improvement of the accuracy of image classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different Pan Sharpening algorithms including Brovey, Gram-Schmidt, NNDiffuse, and Principal Components were applied to merge the Landsat OLI panchromatic band (15 m) with the Landsat OLI multispectral: visible and infrared bands (30 m), to generate a new multispectral image with a higher spatial resolution (15 m). Subsequently, the support vector machine approach was utilized to classify the original Landsat and resulting Pan Sharpened images to generate land cover maps of the study area. The outcomes were then compared through the generation of confusion matrix and calculation of kappa coefficient and overall accuracy. The results indicated superiority of NNDiffuse algorithm in Pan Sharpening and improvement of classification accuracy in Landsat OLI imagery, with an overall accuracy and kappa coefficient of about 98.66% and 0.98, respectively. Furthermore, the result showed that the Gram-Schmidt and Principal Components algorithms also slightly improved the accuracy of image classification compared to original Landsat image. The study concluded that image Pan Sharpening is useful to improve the accuracy of image classification in Landsat OLI imagery, depending on the Pan Sharpening algorithm used for this purpose.

Keyword : Landsat 8 OLI, image classification, support vector machine, land cover mapping, Pan Sharpening

How to Cite
Rokni, K. (2023). Investigating the impact of Pan Sharpening on the accuracy of land cover mapping in Landsat OLI imagery. Geodesy and Cartography, 49(1), 12–18.
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Mar 6, 2023
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Amarsaikhan, D., Saandar, M., Ganzorig, M., Blotevogel, H. H., Egshiglen, E., Gantuyal, R., Nergui, B., & Enkhjargal, D. (2012). Comparison of multisource image fusion methods and land cover classification. International Journal of Remote Sensing, 33, 2532–2550.

Aschbacher, J., & Lichtenegger, J. (1990). Complementary nature of SAR and optical data: A case study in the tropics. Earth Observation Quarterly, 31, 4–8.

Bloom, A. L., Fielding, E. J., & Xiu-Yen, F. (1988). A demonstration of stereo photogrammetry with combined SIR-B and Landsat TM images. International Journal of Remote Sensing, 9, 1023–1038.

Carper, W. J., Lillesand, T. M., & Kiefer, R. W. (1990). The use of intensity-hue saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering & Remote Sensing, 56, 459–467.

Chavez, P. S., Sides, S. C., & Anderson, J. A. (1991). Comparison of three different methods to merge multiresolution and multispectral data- Landsat TM and SPOT panchromatic. Photogrammetric Engineering & Remote Sensing, 57, 295–303.

De Béthune, S., Muller, F., & Donnay, J. P. (1998). Fusion of multispectral and panchromatic images by local mean and variance matching filtering techniques. In Fusion of Earth Data (pp. 28–30), Sophia, Antipolis, France.

Dong, R., Fang, W., Fu, H., Gan, L., Wang, J., & Gong, P. (2021). High-resolution land cover mapping through learning with noise correction. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–13.

Du, P., Liu, S., Xia, J., & Zhao, Y. (2013). Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion, 14, 19–27.

Eskandari, M., Homaee, M., Mahmoodi, S., Pazira, E., & Van Genuchten, M. T. (2015). Optimizing landfill site selection by using land classification maps. Environmental Science and Pollution Research, 22(10), 7754–7765.

Ghayour, L., Neshat, A., Paryani, S., Shahabi, H., Shirzadi, A., Chen, W., Al-Ansari, N., Geertsema, M., Pourmehdi Amiri, M., Gholamnia, M., Dou, J., & Ahmad, A. (2021). Performance evaluation of Sentinel-2 and Landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms. Remote Sensing, 13(7), 1349.

Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., & Hasanlou, M. (2020). Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 276–288.

Gillespie, A. R., Kahle, A. B., & Walker, R. E. (1987). Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques. Remote Sensing of Environment, 22, 343–365.

Hall, D., & Llinas, J. (2001). Handbook of multisensor data fusion. CRC Press.

Helmy, A. K., Nasr, A. H., & El-Taweel, G. S. (2010). Assessment and evaluation of different data fusion techniques. International Journal of Computers, 4, 107–115.

Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., & Rodes, I. (2017). Operational high resolution land cover map production at the country scale using satellite image time series. Remote Sensing, 9(1), 95.

Karathanassi, V., Kolokousis, P., & Ioannidou, S. (2007). A comparison study on fusion methods using evaluation indicators. International Journal of Remote Sensing, 28, 2309–2341.

Klonus, S., & Ehlers, M. (2007). Image fusion using the Ehlers spectral characteristics preservation algorithm. GIScience & Remote Sensing, 44, 93–116.

Klonus, S., & Ehlers, M. (2009). Performance of evaluation methods in image fusion. In 12th International Conference on Information Fusion Seattle (pp. 1409–1416), WA, USA.

Laben, C. A., Bernard, V., & Brower, W. (2000). Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening (U.S. Patent No. 6,011,875). Eastman Kodak Company.

Lawrence, P. J., & Chase, T. N. (2010). Investigating the climate impacts of global land cover change in the community climate system model. International Journal of Climatology, 30(13), 2066–2087.

Leckie, D. (1990). Synergism of synthetic aperture radar and visible/infrared data for forest type discrimination. Photogrammetric Engineering & Remote Sensing, 56, 1237–1246.

Li, Z., Bagan, H., & Yamagata, Y. (2018). Analysis of spatiotemporal land cover changes in Inner Mongolia using self-organizing map neural network and grid cells method. Science of the Total Environment, 636, 1180–1191.

Liu, J. (2000). Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21, 3461–3472.

Meneses, B. M., Pereira, S., & Reis, E. (2019). Effects of different land use and land cover data on the landslide susceptibility zonation of road networks. Natural Hazards and Earth System Sciences, 19(3), 471–487.

Meng, X., Dodson, A., Jixian, Z., Yanhui, C., Chun, L., & Geary, K. (2011). Geospatial data fusion for precision agriculture. In International Symposium on Image and Data Fusion (ISIDF) (pp. 1–4), Tengchong, China.

Nikolakopoulos, K. G. (2008). Comparison of nine fusion techniques for very high resolution data. Photogrammetric Engineering & Remote Sensing, 74, 647–659.

Oetter, D. R., Cohen, W. B., Berterretche, M., Maiersperger, T. K., & Kennedy, R. E. (2001). Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data. Remote Sensing of Environment, 76(2), 139–155.

Pal, M., & Foody, G. M. (2012). Evaluation of SVM, RVM and SMLR for accurate image classification with limited ground data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 1344–1355.

Pérez-Hoyos, A., Rembold, F., Kerdiles, H., & Gallego, J. (2017). Comparison of global land cover datasets for cropland monitoring. Remote Sensing, 9(11), 1118.

Petropoulos, G. P., Kontoes, C. C., & Keramitsoglou, I. (2012). Land cover mapping with emphasis to burnt area delineation using co-orbital ALI and Landsat TM imagery. International Journal of Applied Earth Observation and Geoinformation, 18, 344–355.

Pflugmacher, D., Rabe, A., Peters, M., & Hostert, P. (2019). Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment, 221, 583–595.

Pohl, C., & Van Genderen, J. (1998). Review article Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19, 823–854.

Qiu, X., & Qiu, X. (2011). Wetland monitor for Poyang Lake Ecological Economic Region based on Landsat-7 sensing data. In 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC) (pp. 6495–6498), Deng Feng, China.

Rokni, K., Marghany, M., Hashim, M., & Hazini, S. (2011). Comparative statistical-based and color-related pan sharpening algorithms for ASTER and RADARSAT SAR satellite data. In 2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE) (pp. 618–622), Penang, Malaysia.

Santos, C., & Messina, J. P. (2008). Multi-sensor data fusion for modeling African palm in the Ecuadorian Amazon. Photogrammetric Engineering & Remote Sensing, 74, 711–723.

Schowengerdt, R. A. (1980). Reconstruction of multispatial, multispectral image data using spatial frequency content. Photogrammetric Engineering & Remote Sensing, 46, 1325–1334.

Shakya, A. K., Ramola, A., & Vidyarthi, A. (2021). Exploration of pixel‐based and object‐based change detection techniques by analyzing ALOS PALSAR and LANDSAT Data. In N. Gupta, P. Chatterjee, & T. Choudhury (Eds.), Smart and sustainable intelligent systems (pp. 229–244). Scrivener Publishing.

Solberg, A. H. S., Jain, A. K., & Taxt, T. (1994). Multisource classification of remotely sensed data: Fusion of Landsat TM and SAR images. IEEE Transactions on Geoscience and Remote Sensing, 32, 768–778.

Sood, V., Gusain, H. S., Gupta, S., Taloor, A. K., & Singh, S. (2021). Detection of snow/ice cover changes using subpixel-based change detection approach over Chhota-Shigri glacier, Western Himalaya, India. Quaternary International, 575, 204–212.

Suits, G., Malila, W., & Weller, T. (1988). Procedures for using signals from one sensor as substitutes for signals of another. Remote Sensing of Environment, 25, 395–408.

Sun, W., Chen, B., & Messinger, D. (2014). Nearest neighbor diffusion-based pan-sharpening algorithm for spectral images. Optical Engineering, 53(1), 013107.

Vijayalakshmi, S., Kumar, M., & Arun, M. (2021). A study of various classification techniques used for very high-resolution remote sensing [VHRRS] images. Materials Today: Proceedings, 37, 2947–2951.

Walker, J. J., De Beurs, K. M., Wynne, R. H., & Gao, F. (2012). Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology. Remote Sensing of Environment, 117, 381–393.

Yocky, D. A. (1995). Image merging and data fusion by means of the discrete two dimensional wavelet transform. Journal of the Optical Society of America, 12, 1834–1841.

Zhang, Y. (2002). Automatic image fusion: A new sharpening technique for IKONOS multispectral images. GIM International, 16, 54–57.

Zhang, Y. (2004). Understanding image fusion. Photogrammetric Engineering & Remote Sensing, 70, 657–661.

Zhu, R. (2011). Fusion of airborne SAR image and color aerial image for land use classification. In MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications (Vol. 8006).

Zope, P. E., Eldho, T. I., & Jothiprakash, V. (2017). Hydrological impacts of land use–land cover change and detention basins on urban flood hazard: A case study of Poisar River basin, Mumbai, India. Natural Hazards, 87(3), 1267–1283.