跳到主要导航 跳到搜索 跳到主要内容

Hyperspectral image classification using Gradient Local Auto-Correlations

  • University of Texas at Dallas
  • China University of Geosciences, Wuhan
  • Southeast University, Nanjing
  • Wuhan Textile University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification.

源语言英语
主期刊名Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
出版商Institute of Electrical and Electronics Engineers Inc.
454-458
页数5
ISBN(电子版)9781479961009
DOI
出版状态已出版 - 7 6月 2016
活动3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015 - Kuala Lumpur, 马来西亚
期限: 3 11月 20166 11月 2016

出版系列

姓名Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

会议

会议3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015
国家/地区马来西亚
Kuala Lumpur
时期3/11/166/11/16

学术指纹

探究 'Hyperspectral image classification using Gradient Local Auto-Correlations' 的科研主题。它们共同构成独一无二的学术指纹。

引用此