Development of advanced products for the SEOSAT/Ingenio mission

N. Sabater, A. Ruiz-Verdú, J. Delegido, R. Fernández-Beltrán, P. Latorre-Carmona, F. Pla, M. González-Audícana, J. Álvarez-Mozos, I. Sola, G. Villa, J. A. Tejeiro, E. de Miguel, M. Jimenez, S. Molina, J. Moreno


SEOSAT/Ingenio is the future Spanish Earth Observation high spatial resolution mission in the optical domain. While Level 1 products, at-sensor geo-referenced radiances, are in an advanced phase of development under the framework of an industrial contractor, Level 2 products must be developed by the users. This fact limits the use of the satellite images only to the scientific community, restricting their use in other applications. The need to alleviate this limitation motivated this work, developed under the framework of a coordinate project, which aimed at offering a list of Level2 products to the Ingenio/SEOSAT user community. In this paper, we present the different methodologies developed to produce the proposed Level2 products, from surface reflectance at nominal sensor spatial resolution to images with higher spatial resolution or the possibility to create spatial and temporal mosaics. On the one side, for the surface reflectance product, we proposed an atmospheric correction algorithm based on using the spatial information, linked to a cloud screening algorithm and including morphological and topographic shadow corrections. On the other side, to enhance the image spatial resolution, we applied different fusion techniques using the multispectral and the panchromatic band, as well as some of the so-called “super-resolution” techniques. Finally, we provided different tools to develop spatial mosaics and temporal composites, directed to users interested on the exploitation of the Ingenio/ SEOSAT images.


SEOSAT/Ingenio; Level 2 products; pan-sharpening; super-resolution; atmospheric correction; mosaicking; temporal composites; integration

Full Text:



Blesius, L., Weirich, F. 2005. The use of the Minnaert correction for land-cover classification in mountainous terrain. International Journal of Remote Sensing, 26(17), 3831-3851. https://doi. org/10.1080/01431160500104194

de Lussy, F., Kubik, P., Greslou, D., Pascal, V., Gigord, P., Cantou, J. P. 2005. Pleiades-HR image system products and quality. Proceedings of ISPRS Hannover Workshop 2005: High-Resolution Earth Imaging for Geospatial Information.

Do, M. N., Vetterli, M. 2005. The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 14(12), 2091-2106. https://doi. org/10.1109/TIP.2005.859376

Dong, W., Zhang, D., Shi, G., Wu, X. 2011. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Transactions on Image Processing, 20(7), 1838- 1857.

Fernández Beltrán, R., Latorre-Carmona, P., Pla, F. 2017. Single-frame super-resolution in remote sensing: a practical overview, ISPRS Journal of Photogrammetry and Remote Sensing, 38(1), 314- 354, 27

Freedman, G., Fattal, R. 2011. Image and video upscaling from local self-examples. ACM Transactions on Graphics, 30(2), 1-11. https://doi. org/10.1145/1944846.1944852

Gómez-Chova, L., Camps-Valls, G. Calpe-Maravilla, J., Guanter, L., Moreno, J. 2007. Cloud-screening algorithm for ENVISAT/MERIS multispectral images. IEEE on geoscience and remote sensing. 45(12), 4105-4118. TGRS.2007.905312

González-Audícana, M., Otazu, X., Fors, O., Alvarez- Mozos, J. 2006. A low computational-cost method to fuse IKONOS images using the spectral response function of its sensors. IEEE Transactions on Geoscience and Remote Sensing, 44(6), 1683-1691.

Grodecki, J., Dial, G. 2003. Block adjustment of high resolution satellite images described by rational polynomials, Photogrammetric Engineering & Remote Sensing, 69(1), 59-68. https://doi. org/10.14358/PERS.69.1.59

Irish, R., Baker, J., Goward, S., Arvidson, T. 2006. Characterization of the Landsat-7 ETM+ Automated Cloud Cover Assessment (ACCA) algorithm. Photogrammetric engineering & remote sensing. 72(10), 1179-1188. PERS.72.10.1179

Kaufman, Y. 1982. Solution of the equation of radiative transfer for remote sensing over nonuniform surface reflectivity. Journal of geophysical research. 87(C6), 4137-4147. JC087iC06p04137

Kaufman, Y. 1984. Atmospheric effect on spatial resolution of Surface imagery. Applied Optics, 23(19), 3400-3408. AO.23.003400

Liu, J.G. 2000. Smoothing Filter-based Intensity Modulation: a spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21(18), 3461-3472.

Marini, A., Reina Barragan, F.J., Crippa, G., Harnisch, B., Fuente, I., Lopez, M., Cabeza, I., Zorita, D. 2014. SEOSAT/INGENIO – A Spanish High-spatial-resolution optical mission. International Conference on Space Optics. Tenerife, Spain, 7-10 octubre.

Mekler, Y., Kaufman, Y. 1982. Contrast reduction by the atmosphere and retrieval of nonuniform surface reflectance. Applied Optics, 21(2), 310-316. https://

Otazu, X., González-Audícana, M., Fors, O., Nuñez, J., 2005. Introduction of sensor spectral response into image fusion methods: Application to wavelet-based methods. IEEE Transactions on Geoscience and Remote Sensing. 43(10), 2376-2385. https://doi. org/10.1109/TGRS.2005.856106

Pons, X., Pesquer, L., Cristóbal, J., González-Guerrero, O. 2014. Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images. International Journal of Applied Earth Observation and Geoinformation, 33, 243-254.

Sola, I., González-Audícana, M., Álvarez-Mozos, J., Torres, J.L. 2014. Synthetic images for evaluating Topographic Correction Algorithms. IEEE Transactions on Geoscience and Remote Sensing, 52(3), 1799-1810. TGRS.2013.2255296

Sola, I., González-Audícana, M., Álvarez-Mozos, J. 2015. Validation of a simplified model to generate multispectral synthetic images. Remote Sensing, 7(3), 2942-2951. rs70302942

Sun, J., Xu, Z., Shum, H. Y. 2008. Image super-resolution using gradient profile prior. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-8.

Vicente-Serrano, S.M., Pérez-Cabello, F., Lasanta, T. 2008. Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images. Remote Sensing of Environment 112(10), 3916-3934. https://

Villa, G., Montoro, M.A. 1993. Ajuste radiométrico conjunto de varias imágenes de satélite para la realización de mosaicos de ortoimágenes. En Actas de la V Reunión Científica de la Asociación Española de Teledetección. Las Palmas de Gran Canaria, España, 10 a 12 de Noviembre, pp. 385- 394.

Villa, G., Moreno, J., Calera, A., Amorós-López, J., Camps-Valls, G., Domenech, E., Garrido, J., González-Matesanz, J., Gómez-Chova, L., Martínez, J. A., Molina, S., Peces, J. J., Plaza, N., Porcuna, A., Tejeiro, J. A., Valcárcel, N. 2013. Spectro-temporal reflectance surfaces: a new conceptual framework for the integration of remote-sensing data from multiple different sensors, International Journal of Remote Sensing, 34(9-10), 3699-3715. https://doi.or g/10.1080/01431161.2012.716910

Villa, G., Mas, S., Fernández-Villarino, X., Martínez- Luceño, J., Ojeda, J. C., Pérez-Martín, B., Tejeiro, J. A., García-González, C., López-Romero, E., Soteres, C. 2016. The need of nested grids for aerial and satellite images and Digital Elevation Models. En ISPRS Archives of the XXIII Congress International Society for Photogrammetry and Remote Sensing. Praga, República Checa, 12 - 19 Julio 2016. https://

Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G., Restaino, R., Wald, L. 2015. A Critical Comparison Among Pansharpening Algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2565-2586. https://doi. org/10.1109/TGRS.2014.2361734

Wald, L., Ranchin, T., Mangolini, M. 1997. Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogrammetric Engineering & Remote Sensing, 63(6), 691-699.

Zhang, Y., 2004. Understanding Image Fusion. Photogrammetric Engineering & Remote Sensing, 70(6), 657-661.

Zhang, Y., Kumar, R., 2014. From UNB Pansharp to Fuze Go – the success behind the pan-sharpening algorithms. International Journal of Image and Data Fusion, 5(1), 39-53. 9479832.2013.848475

Zhao, N., Wei, Q., Basarab, A., Kouame, D., Tourneret, J. 2015. Fast single image super-resolution. CoRR

Zhou, J., Civco, D. L., Silander, J. A. 1998. A wavelet transform method to merge Landsat TM and SPOT panchromatic data. International Journal of Remote Sensing, 19(4), 743-757. https://doi. org/10.1080/014311698215973

Abstract Views

Metrics Loading ...

Metrics powered by PLOS ALM

Licencia Creative Commons

This journal is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Universitat Politècnica de València

Official Journal of the Spanish Association of Remote Sensing

e-ISSN: 1988-8740    ISSN: 1133-0953