@inproceedings{82589dce080f40ebb062e5f642bb6042,
title = "Shape-based unmixing for vegetation mapping",
abstract = "Spectral mixture analyses (SMA) is often used as a tool to map complex/mixed (semi-)natural ecosystems.Yet, the performance of SMA, which traditionally uses the amplitudebased RMSE as the objective function, is often hampered by the high spectral similarity among co-occurring plant species. Experiments, based on ray-tracing simulations, in situ measured reflectance data and AVIRIS imagery demonstrated the added value of implementing shape-based error metrics in the unmixing of forests and orchards. The approach allowed to highlight the subtle spectral differences among co-occurring plant species resulting in an overal improvement of species specific mapping (i:e: decrease in MSE ≈ 40%).",
keywords = "Hyperspectral, Spectral Mixture Analysis, forests, orchards, spectral similarity",
author = "L. Tits and B. Somers and {De Keersmaecker}, W. and Asner, {G. P.} and J. Farifteh and P. Coppin",
year = "2012",
doi = "10.1109/WHISPERS.2012.6874222",
language = "English (US)",
isbn = "9781479934065",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012",
note = "2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012 ; Conference date: 04-06-2012 Through 07-06-2012",
}