TY - GEN
T1 - Robust hyperspectral image classification with rejection fields
AU - Condessa, Filipe
AU - Bioucas-DIas, Jose
AU - Kovacevic, Jelena
N1 - Funding Information:
The authors gratefully acknowledge support from the Portuguese Science and Technology Foundation under projects UID/EEA/50008/2013, PTDC/EEI-PRO/1470/2012, the Portuguese Science and Technology Foundation and the CMU-Portugal (ICTI) program under grant SFRH/BD/51632/2011, NSF through award 1017278, and the CMU CIT Infrastructure Award.
Funding Information:
The authors gratefully acknowledge support from the Portuguese Science and Technology Foundation under projects UID/EEA/50008/2013,PTDC/EEI-PRO/1470/2012, the Portuguese Science and Technology Foundation and the CMU-Portugal (ICTI) program under grant SFRH/BD/51632/2011, NSF through award 1017278, and the CMU CIT Infrastructure Award.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/2
Y1 - 2015/7/2
N2 - In this paper we present a novel method for robust hyperspectral image classification using context and rejection. Hyper-spectral image classification is generally an ill-posed image problem where pixels may belong to unknown classes, and obtaining representative and complete training sets is costly. Furthermore, the need for high classification accuracies is frequently greater than the need to classify the entire image. We approach this problem with a robust classification method that combines classification with context with classification with rejection. A rejection field that will guide the rejection is derived from the classification with contextual information obtained by using the SegSALSA [1] algorithm. We validate our method in real hyperspectral data and show that the performance gains obtained from the rejection fields are equivalent to an increase the dimension of the training sets.
AB - In this paper we present a novel method for robust hyperspectral image classification using context and rejection. Hyper-spectral image classification is generally an ill-posed image problem where pixels may belong to unknown classes, and obtaining representative and complete training sets is costly. Furthermore, the need for high classification accuracies is frequently greater than the need to classify the entire image. We approach this problem with a robust classification method that combines classification with context with classification with rejection. A rejection field that will guide the rejection is derived from the classification with contextual information obtained by using the SegSALSA [1] algorithm. We validate our method in real hyperspectral data and show that the performance gains obtained from the rejection fields are equivalent to an increase the dimension of the training sets.
KW - Hyperspectral image classification
KW - classification with rejection
KW - hidden fields
KW - robust classification
UR - http://www.scopus.com/inward/record.url?scp=84998551047&partnerID=8YFLogxK
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U2 - 10.1109/WHISPERS.2015.8075465
DO - 10.1109/WHISPERS.2015.8075465
M3 - Conference contribution
AN - SCOPUS:84998551047
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2015 7th Workshop on Hyperspectral Image and Signal Processing
PB - IEEE Computer Society
T2 - 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015
Y2 - 2 June 2015 through 5 June 2015
ER -