A spatial-spectral SIFT for hyperspectral image matching and classification
作者: Yanshan LiQingteng LiYan LiuWeixin Xie
作者单位: 1ATR National Key Lab. of Defense Technology, Shenzhen University, Shenzhen 518060, China
2Guangdong Key Laboratory of Intelligent Information Processing, College of information Engineering, Shenzhen University, Shenzhen, China
刊名: Pattern Recognition Letters, 2019, Vol.127 , pp.18-26
来源数据库: Elsevier Journal
DOI: 10.1016/j.patrec.2018.08.032
关键词: Hyperspectral imageSIFTSpatial-spectral featureHSI matchingHSI classification
原始语种摘要: Abstract(#br)The scale-invariant feature transform (SIFT) is known as one of the most robust local invariant feature and is widely applied to image matching and classification. However, There is few studies on SIFT for hyperspectral image (HSI). Hyperspectral image (HSI) embraces the spectral information reflecting the material radiation property and the geometrical relationship of the objects. Thus, HSI provides much more information than gray and color image. Therefore, this paper puts forward a spatial-spectral SIFT for HSI matching and classification by using the geometric algebra as its mathematic tool. It extracts and describes the spatial-spectral SIFT feature in the spatial-spectral domain to exploit both the spectral and spatial information of HSI. Firstly, a spatial-spectral...
全文获取路径: Elsevier  (合作)
影响因子:1.266 (2012)

  • matching 匹配
  • spectral 谱的
  • spatial 空间的
  • image 
  • feature 结构元件
  • invariant 不变量
  • classification 分类
  • geometric 凡何
  • information 报告
  • detector 探测器检波器