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STRUCTURED BINARY FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGERY CLASSIFICATION
Zisha Zhong; Bin Fan; Jun Bai; Shiming Xiang; Chunhong Pan
2017
Conference NameIEEE International Conference on Image Processing
Conference Date2017-9-17
Conference PlaceBeijing, CHINA
AbstractIn this paper, we propose a novel structured binary feature extraction method for hyperspectral image classification. To pursuit high discriminative ability and low memory cost, we resort to applying the learning to hash technique to the traditional spectral-spatial hyperspectral features. We show how the structured information among different kinds of features
and different feature groups can be used to learn discriminative binary features for classification. Experiments on two standard benchmark hyperspectral data sets demonstrate the effectiveness of the proposed method.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20354
Collection模式识别国家重点实验室_先进数据分析与学习
AffiliationNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zisha Zhong,Bin Fan,Jun Bai,et al. STRUCTURED BINARY FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGERY CLASSIFICATION[C],2017.
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