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
会议日期	2017-9-17
会议地点	Beijing, CHINA
英文摘要	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.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/20354]
专题	自动化研究所_模式识别国家重点实验室_遥感图像处理团队
作者单位	National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
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]. 见:. Beijing, CHINA. 2017-9-17.