Discriminative Sparse Representation for Hyperspectral Image Classification: A Semi-Supervised Perspective

Zhaohui Xue,Peijun Du,Hongjun Su,Shaoguang Zhou

doi:10.3390/rs9040386

Abstract

This paper presents a novel semi-supervised joint dictionary learning (S2JDL) algorithm for hyperspectral image classification. The algorithm jointly minimizes the reconstruction and classification error by optimizing a semi-supervised dictionary learning problem with a unified objective loss function. To this end, we construct a semi-supervised objective loss function which combines the reconstruction term from unlabeled samples and the reconstruction–discrimination term from labeled samples to leverage the unsupervised and supervised information. In addition, a soft-max loss is used to build the reconstruction–discrimination term. In the training phase, we randomly select the unlabeled samples and loop through the labeled samples to comprise the training pairs, and the first-order stochastic gradient descents are calculated to simultaneously update the dictionary and classifier by feeding the training pairs into the objective loss function. The experimental results with three popular hyperspectral datasets indicate that the proposed algorithm outperforms the other related methods.

Highlights

Hyperspectral remote sensing sensors can provide plenty of useful information that increases the accurate discrimination of spectrally similar materials of interest and allow for the acquisition of hundreds of contiguous bands for the same area on the surface of the Earth [1]
We implemented a semi-supervised method, semi-supervised joint dictionary learning with logistic loss function (S2 JDL-Log, “Log” for Logistic loss function), which is a variant of the proposed method
We denote by S2 JDL-Sof (“Sof” for soft-max loss function) the proposed method to differentiate them

Summary

Introduction

Hyperspectral remote sensing sensors can provide plenty of useful information that increases the accurate discrimination of spectrally similar materials of interest and allow for the acquisition of hundreds of contiguous bands for the same area on the surface of the Earth [1]. The acquired hyperspectral images have been extensively exploited for classification tasks [2,3,4,5], which aim at assigning each pixel with one thematic class for an object in a scene. 2017, 9, 386 a given dictionary and the encoded sparse vectors carry out the class-label information. Let X = [Xl , Xu ] = [x1 , ..., x N ] ∈ Rn× N be a hyperspectral dataset with an n-dimensional signal for each pixel xi = [ x1 , ..., xn ]T , i ∈ 1, ..., N.

Objectives

Methods

Results

Conclusion