TY - GEN
T1 - Morphological pre-processing for classification of hyperspectral data from urban areas
AU - Benediktsson, J. A.
AU - Palmason, J. A.
AU - Sveinsson, J. R.
N1 - Publisher Copyright: © 2004 IEEE.
PY - 2004
Y1 - 2004
N2 - Classification of hyperspectral data with high spatial resolution is discussed. A method based on mathematical morphology for pre-processing of the hyperspectral data is investigated. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. Then, a morphological profile is constructed based on the repeated use of openings and closings with a differently sized structuring element. In order to apply the morphological approach to hyperspectral data, principal components are computed. Then, the principal components are used as base images for the morphological profiles. The use of extended morphological profiles, based on more than one principal component is proposed. In experiments, two data sets are classified. The proposed method performs well in terms of classification accuracies. It gives similar overall accuracies to statistical approaches.
AB - Classification of hyperspectral data with high spatial resolution is discussed. A method based on mathematical morphology for pre-processing of the hyperspectral data is investigated. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. Then, a morphological profile is constructed based on the repeated use of openings and closings with a differently sized structuring element. In order to apply the morphological approach to hyperspectral data, principal components are computed. Then, the principal components are used as base images for the morphological profiles. The use of extended morphological profiles, based on more than one principal component is proposed. In experiments, two data sets are classified. The proposed method performs well in terms of classification accuracies. It gives similar overall accuracies to statistical approaches.
UR - https://www.scopus.com/pages/publications/84945247053
U2 - 10.1109/WARSD.2003.1295207
DO - 10.1109/WARSD.2003.1295207
M3 - Conference contribution
T3 - 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data
SP - 290
EP - 297
BT - 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data
Y2 - 27 October 2003 through 28 October 2003
ER -