Title
Fusion of Extreme Learning Machine and Graph-Based Optimization Methods for Active Classification of Remote Sensing Images
Abstract
In this letter, we propose an efficient multiclass active learning (AL) method for remote sensing image classification. We fuse the capabilities of an extreme learning machine (ELM) classifier and graph-based optimization methods to boost the classification accuracy while minimizing the user interaction. First, we use the ELM to generate an initial label estimation of the unlabeled image pixels. Then, we optimize a graph-based functional energy that integrates the ELM outputs as an initial estimation of the image structure. As for the ELM, the solution to this multiclass optimization problem leads to a system of linear equations. Due to the sparse Laplacian matrix built from the lattice graph defined on the image pixels, the optimization problem is solved in a linear time. In the experiments, we report and discuss the results of the proposed AL method on two very high resolution images acquired by IKONOS-2 and GoeEye-1, as well as the well-known Pavia University hyperspectral image.
Year
DOI
Venue
2015
10.1109/LGRS.2014.2349538
IEEE Geosci. Remote Sensing Lett.
Keywords
DocType
Volume
estimation,hyperspectral imaging,learning artificial intelligence,linear equations,image classification,remote sensing,image resolution,graph theory,accuracy,lattice graph,optimization,multiclass classification,sparse matrices
Journal
12
Issue
ISSN
Citations 
3
1545-598X
16
PageRank 
References 
Authors
0.68
8
6
Name
Order
Citations
PageRank
Mohamed Bencherif1433.73
Yakoub Bazi267243.66
Abderrezak Guessoum3388.07
Naif Alajlan483950.51
Farid Melgani5110080.98
Haikel Salem Alhichri61479.72