Original paper
Self-Paced Joint Sparse Representation for the Classification of Hyperspectral Images
Volume: 57, Issue: 2, Pages: 1183 - 1194
Published: Sep 5, 2018
Abstract
In this paper, a self-paced joint sparse representation (SPJSR) model is proposed for the classification of hyperspectral images (HSIs). It replaces the least-squares (LS) loss in the standard joint sparse representation (JSR) model with a weighted LS loss and adopts a self-paced learning (SPL) strategy to learn the weights for neighboring pixels. Rather than predefining a weight vector in the existing weighted JSR methods, both the weight and...
Paper Details
Title
Self-Paced Joint Sparse Representation for the Classification of Hyperspectral Images
Published Date
Sep 5, 2018
Volume
57
Issue
2
Pages
1183 - 1194