Hyperspectral Image Classification via Basic Thresholding Classifier

Hyperspectral Image Classification via Basic Thresholding Classifier

ABSTRACT:
We propose a lightweight sparsity-based algorithm, namely, the basic thresholding classifier (BTC), for hyperspectral image (HSI) classification. BTC is a pixelwise classifier which uses only the spectral features of a given test pixel. It performs the classification using a predetermined dictionary consisting of labeled training pixels. It then produces the class label and residual vector of the test pixel. Since incorporating spatial and spectral information in HSI classification is quite an effective way of improving classification accuracy, we extend our proposal to a three-step spatial–spectral framework. First, every pixel of a given HSI is classified using BTC. The resulting residual vectors form a cube which could be interpreted as a stack of images representing residual maps. Second, each residual map is filtered using an averaging filter. Finally, the class label of each test pixel is determined based on minimal residual. Numerical results on public data sets show that our proposal outperforms well-known support vector machine-based techniques and sparsity-based greedy approaches like simultaneous orthogonal matching pursuit in terms of both classification accuracy and computational cost.

Distribution code Version 1.0 -- 01/01/2015 by Mehmet Altan Toksöz, Copyright 2015, Middle East Technical University, Turkey.

The Code is created based on the method described in the following papers:
[1] M. A. Toksoz and I. Ulusoy, “Hyperspectral image classification via basic thresholding classifier,”
IEEE Transactions on Geoscience and Remote Sensing, 2016, doi:10.1109/TGRS.2016.2535458.
[2] M. A. Toksoz and I. Ulusoy, “Classification via ensembles of basic thresholding
classifiers,” IET Computer Vision, 2016, doi:10.1049/ietcvi.2015.0077.
Please cite them.

Email: matoksoz [at] gmail.com

Note: The BTC-WLS algorithm (spatial-spectral) requires wlsFilter which can be obtained from http://www.cs.huji.ac.il/~danix/epd/