Information Technologies in Remote Sensing of the Earth

We describe a method for recognition of template objects in multi- and hyperspectral remote sensing data. The matrices of correlation between spectral channels are compared to correlation matrices of templates. The correlation between these matrices is a measure of similarity (double correlation, DC). Templates are recognized in data using the maximum of DC between a template and a fragment of data. The method is sensitive to spatial variations within the fragment as a complement to maximum likelihood (ML) method of classification based on averaged spectra. We add DC to ML in classification of multitemporal Landsat data stacked like a hyperspectral cube for surface types important for air pollution dispersion. For three surface targets for DC (industrial, dense residential and low intensity residential), similar spectrally but different spatially, the effect of DC measured by improvement of the sum of missed target and false alarm probabilities is 2% - 14%.

References:

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Information Technologies in Remote Sensing of the Earth - RORSE 2018

Proceedings of the 16th Conference (November 12-16, 2018, Moscow, Russia)

Recognition of Earth Surface Categories Based on Correlation Portraits and its Use in Modeling Atmospheric Pollution Dispersion

Section 2. Methods and algorithms for processing remote monitoring data