A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples

The classification of high-dimensional data with too few labeled samples is a major challenge which is difficult to meet unless some special characteristics of the data can be exploited. In remote sensing, the problem is particularly serious because of the difficulty and cost factors involved in ass...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2012-06, Vol.50 (6), p.2287-2302
Main Authors: Sami ul Haq, Q., Linmi Tao, Fuchun Sun, Shiqiang Yang
Format: Article
Language:English
Subjects:
Accuracy
Applied geophysics
Dictionaries
Earth sciences
Earth, ocean, space
ell^{1} -minimization
Exact sciences and technology
Homotopy
hyperspectral data classification
Hyperspectral imaging
Internal geophysics
Kernel
remote sensing
sparse representation
Support vector machines
Vectors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The classification of high-dimensional data with too few labeled samples is a major challenge which is difficult to meet unless some special characteristics of the data can be exploited. In remote sensing, the problem is particularly serious because of the difficulty and cost factors involved in assignment of labels to high-dimensional samples. In this paper, we exploit certain special properties of hyperspectral data and propose an l 1 -minimization -based sparse representation classification approach to overcome this difficulty in hyperspectral data classification. We assume that the data within each hyperspectral data class lies in a very low-dimensional subspace. Unlike traditional supervised methods, the proposed method does not have separate training and testing phases and, therefore, does not need a training procedure for model creation. Further, to prove the sparsity of hyperspectral data and handle the computational intensiveness and time demand of general-purpose linear programming (LP) solvers, we propose a Homotopy-based sparse classification approach, which works efficiently when data is highly sparse. The approach is not only time efficient, but it also produces results, which are comparable to the traditional methods. The proposed approaches are tested for our difficult classification problem of hyperspectral data with few labeled samples. Extensive experiments on four real hyperspectral data sets prove that hyperspectral data is highly sparse in nature, and the proposed approaches are robust across different databases, offer more classification accuracy, and are more efficient than state-of-the-art methods.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2011.2172617