@article{MielkeBoescheRogassetal.2015,
  author    = {Mielke, Christian and B{\"o}sche, Nina Kristine and Rogass, Christian and Kaufmann, Hermann and Gauert, Christoph},
  title     = {New geometric hull continuum removal algorithm for automatic absorption band detection from spectroscopic data},
  series = {Remote sensing letters : an official journal of the Remote Sensing and Photogrammetry Society},
  volume    = {6},
  journal   = {Remote sensing letters : an official journal of the Remote Sensing and Photogrammetry Society},
  number    = {2},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {2150-704X},
  doi       = {10.1080/2150704X.2015.1007246},
  pages     = {97 -- 105},
  year      = {2015},
  abstract  = {Modern imaging spectrometers produce an ever-growing amount of data, which increases the need for automated analysis techniques. The algorithms employed, such as the United States Geological Survey (USGS) Tetracorder and the Mineral Identification and Characterization Algorithm (MICA), use a standardized spectral library and expert knowledge for the detection of surface cover types. Correct absorption feature definition and isolation are key to successful material identification using these algorithms. Here, a new continuum removal and feature isolation technique is presented, named the 'Geometric Hull Technique'. It is compared to the well-established, knowledge-based Tetracorder feature database together with the adapted state of the art techniques scale-space filtering, alpha shapes and convex hull. The results show that the geometric hull technique yields the smallest deviations from the feature definitions of the MICA Group 2 library with a median difference of only 8nm for the position of the features and a median difference of only 15\% for the feature shapes. The modified scale-space filtering hull technique performs second best with a median feature position difference of 16nm and a median difference of 25\% for the feature shapes. The scale-space alpha hull technique shows a 23nm median position difference and a median deviation of 77\% for the feature shapes. The geometric hull technique proposed here performs best amongst the four feature isolation techniques and may be an important building block for next generation automatic mapping algorithms.},
  language  = {en}
}