TY - GEN
T1 - A multi-label Hyperspectral image classification method with deep learning features
AU - Wang, Cong
AU - Zhang, Peng
AU - Zhang, Yanning
AU - Zhang, Lei
AU - Wei, Wei
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/8/19
Y1 - 2016/8/19
N2 - Hyperspectral image (HSI) classification is an important application of HSI analysis, which aims at assigning a class label to each pixel. However, considering that mixed pixels commonly exist in HSI, assigning a unique label to each pixel is imprecise. To better analysis the scene imaged in an HSI, we propose a multi-label hyperspectral image classification approach based on deep learning in this study. First, stacked denoising autoencoder (SDAE) method is used to extract deep features for each pixel without supervision, which can well represent the nonlinearity of the mixed pixels in a high dimensional feature space. Then, multi-label logistic regression method assigns each pixel multi labels. Experimental results on the synthetic data, real hyperspectral data and down-sampling hyperspectral data demonstrate the effectiveness of the proposed method.
AB - Hyperspectral image (HSI) classification is an important application of HSI analysis, which aims at assigning a class label to each pixel. However, considering that mixed pixels commonly exist in HSI, assigning a unique label to each pixel is imprecise. To better analysis the scene imaged in an HSI, we propose a multi-label hyperspectral image classification approach based on deep learning in this study. First, stacked denoising autoencoder (SDAE) method is used to extract deep features for each pixel without supervision, which can well represent the nonlinearity of the mixed pixels in a high dimensional feature space. Then, multi-label logistic regression method assigns each pixel multi labels. Experimental results on the synthetic data, real hyperspectral data and down-sampling hyperspectral data demonstrate the effectiveness of the proposed method.
KW - Hyperspectral image
KW - Logistic regression
KW - Multi-label classification
KW - Stacked denoising autoencoder
UR - http://www.scopus.com/inward/record.url?scp=85007621887&partnerID=8YFLogxK
U2 - 10.1145/3007669.3007742
DO - 10.1145/3007669.3007742
M3 - 会议稿件
AN - SCOPUS:85007621887
T3 - ACM International Conference Proceeding Series
SP - 127
EP - 131
BT - Proceedings of the International Conference on Internet Multimedia Computing and Service, ICIMCS 2016
PB - Association for Computing Machinery
T2 - 8th International Conference on Internet Multimedia Computing and Service, ICIMCS 2016
Y2 - 19 August 2016 through 21 August 2016
ER -