%0 Conference Paper %1 He:2006:ciea %A He, Mingyi %A Zhang, Yifan %A Xie, Yuzhen %A Liang, Na %A Wen, Changyun %B 1ST IEEE Conference on Industrial Electronics and Applications %C Singapore, %D 2006 %I IEEE %K algorithms, genetic programming %P 1--6 %R doi:10.1109/ICIEA.2006.257153 %T Classification of Multi-spectral/Hyperspectral Data using Genetic Programming and Error-correcting Output Codes %X Genetic programming (GP) and error-correcting output codes (ECOC) are combined to develop a new classification method (GP-ECOC) for the multi-class problem solving in this paper. Some additional improvements on the algorithm, modified codeword matrix and group division before classification, are also proposed to settle several existing problems in multi-spectral and hyperspectral data classification. Experimental tests using both multi-spectral and hyper-spectral data are carried out for verification and illustration. It is observed from the obtained results that the classification precision with the newly proposed method is greatly enhanced compared with some existing methods using GP, and the proposed improvements are also effective. The algorithm of GP-ECOC and its improved versions can also be run on multi-terminals, which saves computational cost effectively %@ 0-7803-9514-X

@inproceedings{He:2006:ciea, abstract = {Genetic programming (GP) and error-correcting output codes (ECOC) are combined to develop a new classification method (GP-ECOC) for the multi-class problem solving in this paper. Some additional improvements on the algorithm, modified codeword matrix and group division before classification, are also proposed to settle several existing problems in multi-spectral and hyperspectral data classification. Experimental tests using both multi-spectral and hyper-spectral data are carried out for verification and illustration. It is observed from the obtained results that the classification precision with the newly proposed method is greatly enhanced compared with some existing methods using GP, and the proposed improvements are also effective. The algorithm of GP-ECOC and its improved versions can also be run on multi-terminals, which saves computational cost effectively}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Singapore,}, author = {He, Mingyi and Zhang, Yifan and Xie, Yuzhen and Liang, Na and Wen, Changyun}, biburl = {https://www.bibsonomy.org/bibtex/2d87741da9d9b741f22ddd63e93864209/brazovayeye}, booktitle = {1ST IEEE Conference on Industrial Electronics and Applications}, doi = {doi:10.1109/ICIEA.2006.257153}, interhash = {05f741e96237650818309b4fa42ce01f}, intrahash = {d87741da9d9b741f22ddd63e93864209}, isbn = {0-7803-9514-X}, keywords = {algorithms, genetic programming}, month = {24-26 May}, notes = {INSPEC Accession Number: 9096919 Sch. of Electron. & Inf., Northwestern Polytech Univ., Xi'an;}, pages = {1--6}, publisher = {IEEE}, timestamp = {2008-06-19T17:41:15.000+0200}, title = {Classification of Multi-spectral/Hyperspectral Data using Genetic Programming and Error-correcting Output Codes}, year = 2006 }