CバンドおよびLバンドSARデータを併用した機械学習アルゴリズムによる作付作物分類に関する研究

抄録

We present a crop classification method that uses L-band synthetic aperture radar (SAR) data from PALSAR-2 and C-band SAR data from RADARSAT-2, employing random forests as a classification algorithm, and evaluate it from the perspectives of accuracy. It was calculated that backscatter coefficient (HH, HV and VV) and polarimetric parameters (double bounce, volume scattering, surface scattering, entropy, anisotropy and alpha angle) from RADARSAT-2 and PALSAR-2 quad-polarimetric data. The HH backscatter coefficient was calculated from PALSAR-2 single-polarimetric data. Classification using data from one scene gave better overall accuracy for the RADARSAT-2 quad-polarimetric data than for the PALSAR-2 quad-polarimetric data. For classification using one scene from RADARSAT-2 and one from PALSAR-2, the accuracy was higher than that using only one RADARSAT-2 scene, and the difference was statistically significant. When using data from one scene, it is useful to use both C-band and L-band data. Classification using data from three scenes gave better overall accuracy for the RADARSAT-2 data than for the PALSAR-2 data. However, there was no significant difference in classification between combining three scenes from each of the satellites and using three scenes from only RADARSAT-2. When using data from multiple scenes, only C-band data can be gave sufficient accuracy. However, when using single-polarimetric data, it is not possible to calculate valid variables for classification. Therefore, it is necessary to examine the accuracy of classification using quad-polarimetric data of PALSAR-2.