HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing

12/08/2014
by   Victor Stefan Aldea, et al.
0

Sparse regression methods have been proven effective in a wide range of signal processing problems such as image compression, speech coding, channel equalization, linear regression and classification. In this paper we develop a new method of hyperspectral image classification based on the sparse unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization term is introduced. To further enhance class separability, the algorithm is kernelized using a RBF kernel and the final results are improved by a combination of spatial pre and post-processing operations. We show that our method is competitive with state of the art algorithms such as SVM-CK, KLR-CK, KSOMP and KSSP.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/09/2017

Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images

In this paper, we propose an L1 normalized graph based dimensionality re...
research
07/28/2020

Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing

An efficient spatial regularization method using superpixel segmentation...
research
04/01/2021

A study on the effects of compression on hyperspectral image classification

This paper presents a systematic study the effects of compression on hyp...
research
10/14/2011

Robust Image Analysis by L1-Norm Semi-supervised Learning

This paper presents a novel L1-norm semi-supervised learning algorithm f...
research
08/03/2012

Fast and Accurate Algorithms for Re-Weighted L1-Norm Minimization

To recover a sparse signal from an underdetermined system, we often solv...
research
01/05/2015

Sparse Deep Stacking Network for Image Classification

Sparse coding can learn good robust representation to noise and model mo...
research
12/05/2017

Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing

Sparse hyperspectral unmixing from large spectral libraries has been con...

Please sign up or login with your details

Forgot password? Click here to reset