您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Bilateral filtering inspired locality preserving projections for hyperspectral images

文献类型： 外文期刊

作者： Li, Xinrong 1 ; Pan, Jing 2 ; He, Yuqing 2 ; Liu, Changshu 2 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Inst Plant Nutr & Nat Resources, Beijing 100097, Peoples R China

2.Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China

3.Tianjin Univ Technol & Educ, Sch Elect Engn, Tianjin 300222, Peoples R China

关键词： Locality preserving projections;Bilateral filtering;Dimensionality reduction;Subspace learning;Hyperspectral image classification

期刊名称：NEUROCOMPUTING （ 影响因子：5.719； 五年影响因子：4.986 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： As a high-dimensional data, hyperspectral image contains rich information for agricultural remote sensing classification. Locality preserving projections (LPPs) have been widely used for extracting compact and discriminative information from such high-dimensional data. The objective function of LPP is formulated as a sum of the difference between transformed low dimensional vectors weighed by a function of the difference between images. The weights are crucial for LPP which enforce reduced feature vectors preserving the locality property in the original high dimensional space. In this paper, we borrow the idea of weight design of bilateral filtering to re-design the weights in LPP. The weights in bilateral filtering depend not only on the Euclidean distance of pixels (i.e., spatial weight) but also on the intensity differences (i.e., range weight). Analogously, we design the weights in our improved LPP (called bilateral LPP and abbreviated to BLPP) as a multiplication of a function of Euclidean distance parallel to x(i)-x(j)parallel to of the original images (i.e., spatial weight) and a function of the Euclidean distance parallel to f(x(i))-f(x(j))parallel to of the features extracted from the images (i.e., range weight, a.k.a., feature weight). The spatial weight measures the similarity in spatial space whereas the feature weight measures the similarity in feature space which reveals the content of the images. Thus, the proposed BLPP utilizes both the spatial information and the image content information, which results in higher recognition rate. Experimental results on the Salinas and Indian Pine hyperspectral databases demonstrate the effectiveness of BLPP. (C) 2015 Elsevier B.V. All rights reserved.

  • 相关文献

[1]Two-dimensional nearest neighbor classification for agricultural remote sensing. Li, Xinrong,Tang, Yi. 2014

[2]Set-based similarity learning in subspace for agricultural remote sensing classification. Tang, Yi,Li, Xinrong.

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台