This work proposes a new image analysis tool called Label Consistent Transform Learning (LCTL). Transform learning is a recent unsupervised representation learning approach; we add supervision by incorporating a label consistency constraint. The proposed technique is especially suited for hyper-spectral image classification problems owing to its ability to learn from fewer samples. We have compared our proposed method on state-of-the-art techniques like label consistent KSVD, Stacked Autoencoder, Deep Belief Network and Convolutional Neural Network. Our method yields considerably better results (more than 0.1 improvement in Kappa coefficient) than all the aforesaid techniques.
@article{arxiv.1912.11405,
title = {Label Consistent Transform Learning for Hyperspectral Image Classification},
author = {Jyoti Maggu and Hemant K. Aggarwal and Angshul Majumdar},
journal= {arXiv preprint arXiv:1912.11405},
year = {2019}
}
Comments
A modified version has been accepted at IEEE Geosciences and Remote Sensing Letters