Exploiting a texture framework and high spatial resolution properties of panchromatic images to generate enhanced multi-layer products: Examples of Pleiades and historical CORONA space photographs

ABSTRACT

Remotely sensed high spatio-temporal resolution panchromatic images have been extensively used globally to visually detect and interpret changes in landscape components, create land cover maps via the on-screen manual digitization, and to pan-sharpen multi-spectral images among other uses. Despite this attractive array of uses, lack of distinct spectral signatures for panchromatic images from surface elements, e.g. landscape cover types, creates a drawback in their exploitation during any automated classification process, hence limiting their use in the field of remote sensing for land use/land cover change studies. Moreover, the complexities of some panchromatic data (e.g. CORONA) on the one hand, and the traditional texture computation approach on the other hand present additional hurdles in utilizing panchromatic images. This contribution looks at the possibility of exploiting panchromatic images (e.g. Pleiades and historical CORONA products) for remote sensing applications by (i) proposing a new approach that optimizes and generates new multi-layer datasets from panchromatic images that could be useful, e.g. in image classification analysis, (ii)exploiting the combinatorial texture approach to enhance the products generated by the framework in (i) above, and (iii) assessing the capability of the proposed method to handle complex datasets exemplified, e.g. by CORONA. To evaluate the approach, Kurdistan, Iran and Syria regions are selected for study employing the maximum likelihood classification (MLC) scheme. The MLC results indicate an increase in overall accuracy and Kappa coefficient by 32% and 0.42 (compared to raw CORONA image), and 21% and 0.28 (compared to raw Pleiades image). For Iran and Syria, compared to the raw CORONA image, the MLC results show increase by 35% and 0.47, and 42% and 0.56, respectively. Furthermore, based on the results of the accuracy assessment that show an overall accuracy of 85% and Kappa coefficient of 0.80 for Kurdistan, 94% and 0.92 for Iran, and 96% and 0.95 for Syria, the proposed method can be said to have the potential of handling complex panchromatic datasets such as CORONA.

Acknowledgements

The authors would like to express their gratitude to Professor Jason Ur from Harvard University for directing them to the CORONA Atlas for the Middle East website to obtain geo-referenced images for this study. Also, many thanks to ‘CORONA Atlas for the Middle East’ Centre for Advanced Spatial Technologies (CAST), University of Arkansas and U.S. Geological Survey staff for permitting the download of CORONA data from their website. Dr. Ashty Saleem is grateful for the opportunity offered to him by Curtin University, School of Earth and Planetary Sciences to undertake his postdoctoral studies. J.L. Awange on his part would like to thank the financial support of the Alexander von Humboldt (AvH) Foundation that supported his time at Karlsruhe Institute of Technology (Germany). He is grateful to the good working atmosphere provided by his hosts Prof and Hansjörg Kutterer and Prof Bernhard Heck.

Disclosure statement

No potential conflict of interest was reported by the authors.