# Label Consistent Transform Learning for Hyperspectral Image Classification Source: [Label Consistent Transform Learning for Hyperspectral Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:59:21.344Z Authors: Jyoti Maggu, Hemant K. Aggarwal, Angshul Majumdar Published: 2019-12-11T09:55:54.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()