Label Consistent Transform Learning for Hyperspectral Image Classification

2019-12-11 · Jyoti Maggu, Hemant K. Aggarwal, Angshul Majumdar

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.

📄 PDF Abstract BibTeX arXiv:1912.11405

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Classification General Classification Hyperspectral Image Classification image-classification Image Classification Representation Learning

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