TY - GEN
T1 - ANN classification of OMIS hyperspectral remotely sensed imagery
T2 - 1st International Congress on Image and Signal Processing, CISP 2008
AU - Du, Peijun
AU - Tan, Kun
AU - Zhang, Wei
AU - Yan, Zhigang
PY - 2008
Y1 - 2008
N2 - In order to experiment the performance of some popular ANN algorithms to OMIS (Operational Modular Imaging Spectrometer) hyperspectral image, three widely used ANNs, including Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Fuzzy ARTMAP network and their improvements, are employed and compared. It is concluded that ANN classifiers perform much better than traditional classifiers such as SAM, MLC and MDC, and RBFNN outperforms BPNN and Fuzzy ARTMAP in terms of classification accuracy. It is also concluded that dimensionality reduction by PCA can be effectively used to feature extraction for hyperspectral image classification.
AB - In order to experiment the performance of some popular ANN algorithms to OMIS (Operational Modular Imaging Spectrometer) hyperspectral image, three widely used ANNs, including Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Fuzzy ARTMAP network and their improvements, are employed and compared. It is concluded that ANN classifiers perform much better than traditional classifiers such as SAM, MLC and MDC, and RBFNN outperforms BPNN and Fuzzy ARTMAP in terms of classification accuracy. It is also concluded that dimensionality reduction by PCA can be effectively used to feature extraction for hyperspectral image classification.
UR - https://www.scopus.com/pages/publications/52249114305
U2 - 10.1109/CISP.2008.656
DO - 10.1109/CISP.2008.656
M3 - 会议稿件
AN - SCOPUS:52249114305
SN - 9780769531199
T3 - Proceedings - 1st International Congress on Image and Signal Processing, CISP 2008
SP - 692
EP - 696
BT - Proceedings - 1st International Congress on Image and Signal Processing, CISP 2008
Y2 - 27 May 2008 through 30 May 2008
ER -