STRUCTURED BINARY FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGERY CLASSIFICATION

	STRUCTURED BINARY FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGERY CLASSIFICATION
	Zisha Zhong; Bin Fan; Jun Bai; Shiming Xiang; Chunhong Pan
	2017
会议名称	IEEE International Conference on Image Processing
会议日期	2017-9-17
会议地点	Beijing, CHINA
摘要	In 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.
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20354
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
作者单位	National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zisha Zhong,Bin Fan,Jun Bai,et al. STRUCTURED BINARY FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGERY CLASSIFICATION[C],2017.