Classification of the Yellow River delta area using fully polarimetric SAR measurements

ABSTRACT

In this study, an analysis of the polarimetric synthetic aperture radar (SAR) capabilities to classify coastal areas is undertaken. The Yellow River delta (China) is selected as the test case since it represents an extraordinary environmental and economical area, which is characterized by a very heterogeneous scattering scenario, as witnessed by official reference data, provided by the Chinese government, that classified 12 different kinds of environment. Experimental results, obtained applying two well-known unsupervised classifiers, namely the H/α-based and the Freeman–Durden model-based algorithms, to a fully polarimetric SAR scene collected by Radarsat-2 in 2008 are compared and critically discussed. Both provide a satisfactory global accuracy (larger than 60% in average) with reference to the inland Yellow River delta area, but there are subareas that result in misclassifications and severe classification ambiguities. This study also suggests including single-polarization intensity information to improve the classification accuracy and to partly solve ambiguities.

Acknowledgements

This study is partly funded by the European Space Agency (ESA) in the framework of the Dragon 4 cooperation between ESA and Chinese Ministry of Science and Technology, project ID 32235, and by the Università degli Studi di Napoli Parthenope, project ID DING202. We acknowledge the Canadian Space Agency for providing the Radarsat-2 SAR data under the Science and Operational Applications Research (SOAR)-EI project ID 5155 entitled ‘RadarSAT-2 polSAR data for coastal zone monitoringʼ. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official NOAA or US Government position, policy, or decision.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the European Space Agency (ESA) and Chinese Ministry of Science and Technology: [Grant Number 32235]; Università degli Studi di Napoli Parthenope: [Grant Number DING202].