Skip to main content
padlock icon - secure page this page is secure

Hyperspectral image classification based on robust discriminative extraction of multiple spectral-spatial features

Buy Article:

$60.00 + tax (Refund Policy)

In hyperspectral image (HSI) processing, the inclusion of both spectral and spatial features, e.g. morphological features, shape features, has shown great success in classification of hyperspectral data. Nevertheless, there exist two main issues to address: (1) The multiple features are often treated equally and thus the complementary information among them is neglected. (2) The features are often degraded by a mixture of various kinds of noise, leading to the classification accuracy decreased. In order to address these issues, a novel robust discriminative multiple features extraction (RDMFE) method for HSI classification is proposed. The proposed RDMFE aims to project the multiple features into a common low-rank subspace, where the specific contributions of different types of features are sufficiently exploited. With low-rank constraint, RDMFE is able to uncover the intrinsic low-dimensional subspace structure of the original data. In order to make the projected features more discriminative, we make the learned representations optimal for classification. With intrinsic information preserving and discrimination capabilities, the learned projection matrix works well in HSI classification tasks. Experimental results on three real hyperspectral datasets confirm the effectiveness of the proposed method.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: 1: College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China 2: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

Publication date: August 3, 2019

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more