ICA-Aided Mixed-Pixel Analysis of Hyperspectral Data in Agricultural Land
Naoko Kosaka, Kuniaki Uto, Yohei Kosugi · IEEE Geoscience and Remote Sensing Letters · 2005
This letter proposes an independent component analysis (ICA)-aided mixed-pixel analysis of periodically distributed hyperspectral data in agricultural land. This method simultaneously estimates the pure spectra and coverage of endmembers, such as crop and soil, from mixed-pixel data which is inevitably included in images observed from high-altitude sensors. The method is effective for agricultural management because the change of observed mixed-pixel data is distinguished into a qualitative spectral one, due to chlorophyll quantity or crop variety, and the quantitative coverage due to growth stages. This method introduces a priori knowledge which is independent of the type of crop and effective in deriving a scaling factor for the independent component (IC), estimated from the ICA process. The fundamental investigation, using hyperspectral data obtained from a crane and an aircraft, shows the applicability of the method.