Hyperspectral Image Interpretation: A Critical Step in Remote Sensing Applications
Hyperspectral image interpretation serves as a vital component in remote sensing applications. Spectral interpretation techniques based on the Linear Spectral Mixture Model (LSMM) are gaining widespread adoption. Simulation results demonstrate that incorporating partial endmembers and implementing region-based interpretation significantly enhances hyperspectral image analysis outcomes. This approach involves implementing endmember extraction algorithms (e.g., N-FINDR) and region segmentation methods to optimize spectral unmixing performance.