Can spectral angle mapper classification be used for hyperspectral and panchromatic fused image?

 Yes, spectral angle mapper (SAM) classification can be used for hyperspectral and panchromatic fused images. In fact, SAM classification is a popular method for classifying hyperspectral data and has been widely used in remote sensing applications.

When it comes to hyperspectral and panchromatic fused images, SAM classification can be applied by using the spectral information from the hyperspectral data and the spatial information from the panchromatic data. The spectral information from the hyperspectral data is used to calculate the spectral angles between the reference spectra and the pixels in the image, while the panchromatic data is used to provide spatial detail and improve the accuracy of the classification.

The fusion of hyperspectral and panchromatic data provides high spectral and spatial resolution, which is useful for a wide range of remote sensing applications such as land cover classification, mineral mapping, and urban area mapping. SAM classification can be particularly useful in these applications because it is a robust and flexible method that can handle a large number of spectral bands and can effectively distinguish between different spectral signatures.

In summary, SAM classification can be used for hyperspectral and panchromatic fused images by using the spectral information from the hyperspectral data and the spatial information from the panchromatic data to classify the image.