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通过图像融合技术提高 Sentinel-2 监测海洋塑料垃圾的能力。

Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniques.

机构信息

Laboratory of Remote Sensing, National Technical University of Athens, School of Rural, Surveying, and Geoinformatics Engineering, Zografou 15780, Greece.

Laboratory of Remote Sensing, National Technical University of Athens, School of Rural, Surveying, and Geoinformatics Engineering, Zografou 15780, Greece.

出版信息

Mar Pollut Bull. 2022 Sep;182:113974. doi: 10.1016/j.marpolbul.2022.113974. Epub 2022 Jul 30.

Abstract

Sentinel-2 (S2) images have been used in several projects to detect large accumulations of marine litter and plastic targets. Their limited spatial resolution though hinders the detection of relatively small floating accumulations of marine debris. Thus, this study aims at overcoming this limit through the exploration of fusion with very high-resolution WorldView-2/3 (WV-2/3) images. Various state-of-the-art approaches (component substitution, spectral unmixing, deep learning) were applied on data collected in synchronized acquisitions of plastic targets of various sizes and materials in seawater. The fused images were evaluated for spectral and spatial distortions, as well as their ability to spectrally discriminate plastics from water. Several WV-2/3 band combinations were investigated and five litter indexes were applied. Results showed that: a) the VNIR combination is the optimal one, b) the smallest observable plastic target is 0.6 × 0.6 m and c) SWIR bands are important for marine litter detection.

摘要

哨兵-2 (S2)图像已被用于多个项目,以检测海洋垃圾和塑料目标的大量积聚。然而,其有限的空间分辨率阻碍了对相对较小的漂浮海洋碎片积聚的检测。因此,本研究旨在通过探索与超高分辨率 WorldView-2/3(WV-2/3)图像的融合来克服这一限制。各种最先进的方法(分量替换、光谱解混、深度学习)应用于在海水中同步采集的各种尺寸和材料的塑料目标数据上。评估了融合图像的光谱和空间失真,以及它们从水中区分塑料的能力。研究了几种 WV-2/3 波段组合,并应用了五个垃圾指数。结果表明:a)VNIR 组合是最优的,b)可观测到的最小塑料目标是 0.6×0.6 米,c)SWIR 波段对海洋垃圾检测很重要。

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