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As an entrypoint into endmember selection, we should investigate the robust successive projection algorithm (RSPA). This is a means to determine, in a noise-tolerant way, a set of spectral endmembers that describe the dominant spectral components present in a sample of spectra. These can then be used in unmixing operations to disaggregate the pixels of an image according to the abundance of each endmember's material.
References:
Gillis, N. (2019, December). Successive projection algorithm robust to outliers. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (pp. 331-335). IEEE.
The text was updated successfully, but these errors were encountered:
As an entrypoint into endmember selection, we should investigate the robust successive projection algorithm (RSPA). This is a means to determine, in a noise-tolerant way, a set of spectral endmembers that describe the dominant spectral components present in a sample of spectra. These can then be used in unmixing operations to disaggregate the pixels of an image according to the abundance of each endmember's material.
References:
Gillis, N. (2019, December). Successive projection algorithm robust to outliers. In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (pp. 331-335). IEEE.
The text was updated successfully, but these errors were encountered: