Remote sensing systems are commonly classified on the basis of their spectral resolution into multispectral and hyperspectral systems, each having distinct characteristics, capabilities, and data processing requirements. Multispectral remote sensing involves the acquisition of image data in a limited number of broad, discrete spectral bands, typically ranging from three to a few tens. These bands are usually placed in key regions of the electromagnetic spectrum such as visible, near-infrared, and shortwave infrared to capture major variations in surface features. Hyperspectral remote sensing, on the other hand, acquires data in a very large number of contiguous and narrow spectral bands, often numbering in the hundreds, enabling the detailed characterization of the spectral signature of materials. The fundamental difference between multispectral and hyperspectral remote sensing lies in their ability to resolve spectral information. Multispectral sensors, such as Landsat or Sentinel-2, provide moderate spectral resolution, which is sufficient for general land cover classification, vegetation monitoring, water body analysis, and urban studies. Hyperspectral sensors, such as AVIRIS