Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Rudolf Ressel, Suman Singha, Susanne Lehner, Anja Rosel, Gunnar Spreen
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2016
• Subjects: Artificial neural network (ANN); feature evaluation; polarimetry; sea ice classification; TerraSAR-X; Ocean engineering; TC1501-1800; Geophysics. Cosmic physics; QC801-809; Sea ice
• Online Access: https://doi.org/10.1109/JSTARS.2016.2539501; https://doaj.org/article/3084bfc6b1ed438abbb98f9bca5a9938

Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH-VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to be more useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use.