Exploratory search methodology for sentinel 2 data: A prospect of both visual and latent characteristics

Abstract

Sentinel 2 (S2) satellite provides a systematic global coverage of land surfaces, measuring physical properties within 13 spectral intervals at a temporal resolution of 5 days. Computer-based data analysis is highly required to extract similarity by processing and assist human understanding and semantic annotation in support of Earth surface mapping. This paper proposes an exploratory search methodology for S2 data underpinning both visual and latent characteristics by means of data visualization and content representation. For optimized results, the authors focus on a detailed assessment of top relevant state-of-the-art algorithms for features extraction and classification to determine which one could handle best the characteristics of S2 data. Index Terms-exploratory multispectral data analysis Precision Recall Class Name 93.98% 87.54% Agriculture-HV 91.90% 82.41% Agriculture-LV 73.78% 89.52% Forest 86.49% 96.55% Urban 88.35% 81.98% Water