open access

https://hdl.handle.net/2115/71450

Crop Classification by Machine Learning Algorithm Using C-band SAR Data

日本語

2017-09-08

日本写真測量学会

Journal of the Japan society of photogrammetry and remote sensing

56(4)

143-148

This paper presents crop classification using satellite data to establish a mapping method to replace the existing ground survey. We used five scenes of C-band fully polarimetric SAR satellite Radarsat-2 data. Datasets of sigma naught and four polarimetric parameters, Freeman－Durden (FD), Van Zyl (VZ), Yamaguchi (YG), and Cloude－Pottier (CP), were calculated from each image data. We assessed the accuracy of the classification obtained by the random forest machine learning algorithm. Three results are shown. First, the highest accuracy using only one of the five datasets (0.918) was obtained by the VZ parameter dataset. Second, using three datasets, the combination of the sigma naught, VZ parameter, and CP parameter datasets obtained the highest accuracy (0.922). Third, when we used all five datasets, the accuracy (0.918) was not improved. These results confirm that crop classification using Radarsat-2 C-band data is very effective and the use of a combination of sigma naught, VZ parameters, and CP parameters obtained the highest accuracy.

学術雑誌論文

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© 2017 Japan Society of Photogrammetry and Remote Sensing

0285-5844

1883-9061