Automatic wide area land cover mapping using Sentinel-1 multitemporal data

D. Marzi, Antonietta Sorriso, Paolo Gamba
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Abstract

This study introduces a methodology for land cover mapping across extensive areas, utilizing multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) data. The objective is to effectively process SAR data to extract spatio-temporal features that encapsulate temporal patterns within various land cover classes. The paper outlines the approach for processing multitemporal SAR data and presents an innovative technique for the selection of training points from an existing Medium Resolution Land Cover (MRLC) map. The methodology was tested across four distinct regions of interest, each spanning 100 × 100 km2, located in Siberia, Italy, Brazil, and Africa. These regions were chosen to evaluate the methodology’s applicability in diverse climate environments. The study reports both qualitative and quantitative results, showcasing the validity of the proposed procedure and the potential of SAR data for land cover mapping. The experimental outcomes demonstrate an average increase of 16% in overall accuracy compared to existing global products. The results suggest that the presented approach holds promise for enhancing land cover mapping accuracy, particularly when applied to extensive areas with varying land cover classes and environmental conditions. The ability to leverage multitemporal SAR data for this purpose opens new possibilities for improving global land cover maps and their applications.
利用 Sentinel-1 多时数据自动绘制大面积土地覆被图
本研究介绍了一种利用多时相Sentinel-1合成孔径雷达(SAR)数据进行大面积土地覆盖制图的方法。目标是有效地处理SAR数据,以提取包含不同土地覆盖类别时间模式的时空特征。本文概述了处理多时相SAR数据的方法,并提出了一种从现有的中分辨率土地覆盖(MRLC)地图中选择训练点的创新技术。该方法在西伯利亚、意大利、巴西和非洲四个不同的区域进行了测试,每个区域的面积为100 × 100平方公里。选择这些地区是为了评估该方法在不同气候环境中的适用性。该研究报告了定性和定量结果,显示了拟议程序的有效性和SAR数据用于土地覆盖制图的潜力。实验结果表明,与现有的全球产品相比,总体精度平均提高了16%。结果表明,所提出的方法有望提高土地覆盖制图的精度,特别是当应用于具有不同土地覆盖类别和环境条件的广泛地区时。为此目的利用多时SAR数据的能力为改进全球土地覆盖图及其应用开辟了新的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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