Bi-Level Sparse Coding: A Hyperspectral Image Classification Example⁎
Zhangyang Wang Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States
Abstract
We present a semisupervised method for single pixel classification of hyperspectral images. The proposed method is designed to address the special problematic characteristics of hyperspectral images, namely, high dimensionality of hyperspectral pixels, lack of labeled samples, and spatial variability of spectral signatures. To alleviate these problems, the proposed method features the following components. First, being a semisupervised approach, it exploits the wealth of unlabeled samples in the image by evaluating the confidence ...
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