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The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through the use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m. [ABSTRACT FROM AUTHOR]

The author focuses on publications related to remote sensing. He mentions that as of December 2013 there are 27 journals related to remote-sensing in the online subscription-based scientific citation indexing service Web of Science. He also mentions the Journal Citation Reports, an annual publication related to academic journals in the sciences and social sciences and also talks about significant increase in research papers on remote sensing.