Volume XLII-1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 451-456, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-451-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 451-456, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-451-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Sep 2018

26 Sep 2018

AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

M. Ustuner1, F. B. Sanli1, S. Abdikan2, M. T. Esetlili3, and G. Bilgin4 M. Ustuner et al.
  • 1Dept. of Geomatic Engineering, Yildiz Technical University, Istanbul, Turkey
  • 2Dept. of Geomatics Engineering, Bulent Ecevit University, Zonguldak, Turkey
  • 3Dept. of Soil Science and Plant Nutrition, Ege University, Izmir, Turkey
  • 4Dept. of Computer Engineering, Yildiz Technical University, Istanbul, Turkey

Keywords: Polarimetric SAR, Crop Classification, Multi-temporal Data, Agriculture, Synthetic Aperture Radar

Abstract. Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (α) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) Hα, (2) HαSpan, (3) HαA, (4) HαASpan and (5) coherency [T] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that HαASpan (91.43 % for SVM, 92.25 % for RF and 90.55 % for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25 % by RF and HαASpan while lowest classification accuracy was obtained as 66.99 % by NB and Hα. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification.