Contribution of Minimum Noise Fraction Transformation of Multi-temporal RADARSAT-2 Polarimetric SAR Data to Cropland Classification

Abstract

Agriculture is an important sector in Canada, and annual crop inventories are required in many agricultural applications. Multi-temporal polarimetric synthetic aperture radar (SAR) data have great potential in crop classification due to its less dependency on weather condition. This study, for the first time, investigated the effects of the Minimum Noise Fraction (MNF) transformation of multi-temporal RADARSAT-2 polarimetric SAR data on the performance of cropland classification through the discussing of the performance of different polarimetric SAR parameter sets, and the impact of the timing of RADARSAT-2 datasets in southwestern Ontario. The random forest classifier was adopted due to its excellent ability in crop classification. The results illustrated that the elements of coherency matrix performed the best in agricultural land cover classification. The multi-temporal polarimetric SAR data acquired from the end of June to November gave the best classification accuracy, and an overall accuracy of 90% can be achieved using two images acquired in the middle of September and October. The MNF transformation can further improve the classification accuracy, and this accuracy was competitive with the accuracy produced using the integration of optical and polarimetric SAR data.