@inproceedings{4895eb79bf764ef88249e1a91b69f60c,
title = "Spectral unmixing for hyperspectral image classification with an adaptive endmember selection",
abstract = "Hyperspectral classification techniques are widely used for detailed analysis of the earth surface. However, mixed pixels caused by the relatively low spatial resolution of the imaging system are the big burden for traditional pure-pixel-hypothesis based hard classification methods. To address this problem, a novel method, which jointly uses soft classification and spectral unmixing, is proposed in this paper. The confusion matrix is exploited to determine the endmember set for each class. Then the generated endmember is adopted for spectral unmixing. The fractional abundance of training samples, which is generated from spectral unmixing, is utilized to optimize soft multinomial logistic regression classifier. The result of the optimized classifier will result in a more accurate confusion matrix. Thus, this procedure is executed iteratively to achieve required performance. Experimental results on synthetic and real hyperspectral data sets demonstrate the superiority of the proposed method for hyperspectral image classification.",
keywords = "endmember selection, hyperspectral image, spectral unmixing, supervised classification",
author = "Qingjie Meng and Yanning Zhang and Wei Wei and Lei Zhang",
year = "2013",
doi = "10.1007/978-3-642-42057-3_46",
language = "英语",
isbn = "9783642420566",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "359--367",
booktitle = "Intelligence Science and Big Data Engineering - 4th International Conference, IScIDE 2013, Revised Selected Papers",
note = "4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013 ; Conference date: 31-07-2013 Through 02-08-2013",
}