# Label Consistent Transform Learning for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/1912.11405 · HTML: https://mlsanity.com/paper/1912.11405/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Transformed K-means Clustering](https://mlsanity.com/paper/2111.13921/) (2021)

## Abstract (arXiv)

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.