Comparison of algorithms and input vectors for sea-ice classification with l-band POLSAR data
Journal
2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019 - Proceedings
Pages
2141-2142
Date Issued
2019
Author(s)
Yang, K.-S.
Abstract
Convolutional neural network (CNN) and fuzzy c-means (FCM) are applied to L-band PolSAR data in advanced-melt and winter phases, respectively, to classify sea-ice type. Several types of input vector are derived from the covariance matrix to improve the accuracy rate of classification. The parameters in algorithms and input vectors are also analyzed.
SDGs
Type
conference paper