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module fusionrs

function fusionrs

1
fusionrs(optical, radar, stand_varb=True, nodata=-99999, **kwargs)

Fusion of images with different observation geometries through Principal Component Analysis (PCA).

This algorithm allows to fusion images coming from different spectral sensors (e.g., optical-optical, optical and SAR, or SAR-SAR). It is also possible to obtain the contribution (%) of each variable in the fused image.

Parameters:

  • optical: Optical image. It must be rasterio.io.DatasetReader with 3d.

  • radar: Radar image. It must be rasterio.io.DatasetReader with 3d.

  • stand_varb: Logical. If stand.varb = True, the PCA is calculated using the correlation matrix (standardized variables) instead of the covariance matrix (non-standardized variables).

  • nodata: The NoData value to replace with -99999.

  • **kwargs: These will be passed to scikit-learn PCA, please see full lists at:

  • https: //scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

Return:

A dictionary.

References:

Tarazona, Y., Zabala, A., Pons, X., Broquetas, A., Nowosad, J., and Zurqani, H.A. Fusing Landsat and SAR data for mapping tropical deforestation through machine learning classification and the PVts-β non-seasonal detection approach, Canadian Journal of Remote Sensing., vol. 47, no. 5, pp. 677–696, Sep. 2021.

Note:

Before executing the function, it is recommended that images coming from different sensors or from the same sensor have a co-registration. The contributions of variables in accounting for the variability in a given principal component are expressed in percentage. Variables that are correlated with PC1 (i.e., Dim.1) and PC2 (i.e., Dim.2) are the most important in explaining the variability in the data set. Variables that do not correlated with any PC or correlated with the last dimensions are variables with low contribution and might be removed to simplify the overall analysis. The contribution is a scaled version of the squared correlation between variables and component axes (or the cosine, from a geometrical point of view) --- this is used to assess the quality of the representation of the variables of the principal component, and it is computed as (cos(variable,axis)^2/total cos2 of the component)×100.

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