Aptoula,E.Koc,S.G.2024-05-252024-05-2520184978-153867150-410.1109/IGARSS.2018.85193512-s2.0-85064165004https://doi.org/10.1109/IGARSS.2018.8519351https://hdl.handle.net/20.500.14517/2394Geoscience and Remote Sensing Society (GRSS); The Institute of Electrical and Electronics Engineers (IEEE)Morphological attribute profiles are among the most prominent spatial-spectral pixel description methods. They are efficient, highly flexible multiscale tools that operate at the connected component level of images. One of their few yet significant drawbacks is their need for a predefined threshold set. As such there have been multiple attempts for computing thresholds with minimal or no supervision with various levels of success. In this paper, a radically different approach is taken and a new way is presented, circumventing the need for thresholds while harnessing the descriptive power of the hierarchical tree representation underlying the attribute profiles. The introduced approach is validated with two datasets and two attributes, where it exhibits either comparable or superior performance to manual and automatic threshold based attribute profiles. © 2018 IEEE.eninfo:eu-repo/semantics/closedAccessAttribute profilesHyperspectral imagesPixel classificationSupervised classificationTree representationConference Object2018-July45074510