Evaluation of advanced time series similarity measures for object-based cropland mapping

ABSTRACT

With more and more high-resolution remote-sensing data becoming available, satellite image time series (SITS) analysis is often used to map crops at the pixel level. Although the object-based image analysis paradigm has proven superior to pixel-based image analysis for overcoming salt-and-pepper effects, object-based time series crop classification is still rare. Except for time-weighted dynamic time warping (TWDTW), other time series similarity measuring algorithms popular in the signal processing domain, such as shape-based distance (SBD) and global alignment kernel (GAK), remain poorly explored at the object level. Accordingly, this study aimed to explore better object-based time series classification frameworks by investigating similarity measures, including SBD, GAK and TWDTW, and thus experiments were designed to analyse the response of these methods to various parameters, including crop types, time series density, processing efficiency and so on. The results show that GAK is superior to SBD and TWDTW in general. Specifically, GAK performs better than TWDTW with the limited available images, e.g. in cloudy and rainy climate regions. Also, GAK outperforms SBD in identifying crops with a complex phenological pattern, such as alfalfa. Furthermore, TWDTW has the lowest processing efficiency. This study addresses the scarcity of similarity measurement methods in the object-based time series paradigm, and it is expected that this study could provide some guidance for the selection of cropland mapping methods.

KEYWORDS:

• Crop classification
• satellite image time series
• object-based image analysis
• time series similarity measures

Acknowledgements

Sincere thanks to the anonymous reviewers and members of the editorial team for the comments and contributions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available from the Copernicus Open Access Hub at https://scihub.copernicus.eu/dhus/#/home.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42171304]; Sichuan Science and Technology Program [2023YFS0380]; State Key Laboratory of Hydraulics and Mountain River Engineering [SKHL2210].