1 January 2011 Extraction of sea ice concentration based on spectral unmixing method
Author Affiliations +
J. of Applied Remote Sensing, 5(1), 053552 (2011). doi:10.1117/1.3643703
The traditional methods to derive sea ice concentration are mainly from low resolution microwave data, which is disadvantageous to meet the grid size requirement of high resolution climate models. In this paper, moderate resolution imaging spectroradiometer (MODIS)/Terra calibrated radiances Level-1B (MOD02HKM) data with 500 m resolution in the vicinity of the Abbot Ice Shelf, Antarctica, is unmixed, respectively, by two neural networks to extract the sea ice concentration. After two different neural network models and MODIS potential open water algorithm (MPA) are introduced, a MOD02HKM image is unmixed using these neural networks and sea ice concentration maps are derived. At the same time, sea ice concentration for the same area is extracted by MPA from MODIS/Terra sea ice extent (MOD29) data with 1 km resolution. Comparisons among sea ice concentration results of the three algorithms showed that a spectral unmixing method is suitable for the extraction of sea ice concentration with high resolution and the accuracy of radial basis function neural network is better than that of backpropagation.
Dong Zhang, Changqing Ke, Bo Sun, Ruibo Lei, Xueyuan Tang, "Extraction of sea ice concentration based on spectral unmixing method," Journal of Applied Remote Sensing 5(1), 053552 (1 January 2011). https://doi.org/10.1117/1.3643703

Neural networks

Image resolution

Spectral resolution



Data modeling

Evolutionary algorithms

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