The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 257–264, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-257-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 257–264, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-257-2021

  28 Jun 2021

28 Jun 2021

ENHANCING UAV COASTAL MAPPING USING INFRARED PANSHARPENING

D. James1, A. Collin1,2, A. Mury1, and M. Letard1 D. James et al.
  • 1EPHE, Université PSL, CNRS UMR 6554 LETG, 35800 Dinard, France
  • 2LabEx CORAIL, Moorea, French Polynesia

Keywords: UAV, Multispectral, Coastal, Pansharpening, Classification, Ecosystem

Abstract. Ecosystems must now cope with climate change such as rising sea levels. These major changes have a direct impact on the coastal fringe. However, in recent years, coastal ecosystems such as saltmarshes have proven their adaptive capacity. Unmanned Aerial Vehicles (UAV) are an inexpensive and easily deployable alternative which offer us the possibility to monitor these geomorphological and ecological systems, have been perfected over the years, making it possible to achieve high or even very high (VH) spectral and spatial resolution. Detection of changes at VH temporal and spatial resolution such as coastline evolution or seasonal monitoring of plant communities is facilitated. The red-green-blue (RGB) camera is the basic equipment of low-cost UAVs. Many studies have demonstrated the interest of infrared sensors for vegetation or water detection. In this original study, a pansharpening method has been developed to generate a red-edge (RE) and near infrared channel based on the VH resolution of RGB. Out of the three different pansharpening algorithms tested, Gram-Schmidt showed correlation (0.61 and 0.63 for RE and NIR channels respectively), followed by nearest neighbor diffusion and finally, principal component spectral pansharpening. The maximum likelihood, support vector machine and convolutional neural network classifiers were used to discriminate the main objects of the study area. The classification results revealed that at the classifier scale the ML outperforms the others with an overall accuracy of 80.75%. At the spectral band scale, the RE obtains the best performances with 80.04% of OA with ML and 78.34% of OA with SVM.