open access

https://hdl.handle.net/2115/71286

Assessing the Suitability of Data from Sentinel-1A and 2A for Crop Classification

英語

Sentinel-2A

2017-07-10

Taylor & Francis

GIScience & Remote Sensing

54(6)

918-938

Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches of kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarisation data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.

学術雑誌論文

Accepted Manuscript

This is an Accepted Manuscript of an article published by Taylor & Francis in GIScience & Remote Sensing on 10 Jul 2017, available online: http://www.tandfonline.com/doi/full/10.1080/15481603.2017.1351149.

1548-1603

1943-7226