The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 151–157, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-151-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 151–157, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-151-2022
 
30 May 2022
30 May 2022

HOW CHALLENGING IS THE DISCRIMINATION OF FLOATING MATERIALS ON THE SEA SURFACE USING HIGH RESOLUTION MULTISPECTRAL SATELLITE DATA?

P. Mikeli1, K. Kikaki1,2, I. Kakogeorgiou1, and K. Karantzalos1 P. Mikeli et al.
  • 1Remote Sensing Laboratory, National Technical University of Athens, Athens, Zografou, Greece
  • 2Institute of Oceanography, Hellenic Centre for Marine Research, Athens, Anavyssos, Greece

Keywords: marine, debris, litter, MARIDA, Sentinel-2, GLCM, FDI, NDVI

Abstract. Developing a remote sensing-based monitoring system for detecting marine plastics requires a thorough investigation of their discrimination possibilities from other floating objects. To this end, this study aims to explore the ability to discriminate marine debris from other floating materials and sea features using high-resolution multispectral satellite data. To perform our analysis, we utilized the open-access Marine Debris Archive (MARIDA), which contains several marine classes on Sentinel-2 (S2) data and benchmarks machine learning frameworks. In particular, we investigated well-established spectral indices, GrayLevel Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) texture and other spatial features at multiple scales. A Random Forest (RF) classifier was also applied for the classification procedure, and the spectral and spatial features which contributed the most were underlined. The quantitative and qualitative assessment indicated that the spectral information alone is insufficient to distinguish marine plastic from other floating materials which exhibit similar spectral behavior, such as vessels. However, a strong potential even for challenging discrimination tasks is presented when combined with spatial information. By further evaluating our results qualitatively, significant insights are gained, and specific combinations are proposed for challenging floating materials discrimination.