Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification
- 23 August 2010
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 48 (11), 4099-4109
- https://doi.org/10.1109/tgrs.2010.2055876
Abstract
Approaches to combine local manifold learning (LML) and the k -nearest-neighbor (kNN) classifier are investigated for hyperspectral image classification. Based on supervised LML (SLML) and kNN, a new SLML-weighted kNN (SLML-W kNN) classifier is proposed. This method is appealing as it does not require dimensionality reduction and only depends on the weights provided by the kernel function of the specific ML method. Performance of the proposed classifier is compared to that of unsupervised LML (ULML) and SLML for dimensionality reduction in conjunction with the kNN (ULML- kNN and SLML-k NN). Three LML methods, locally linear embedding (LLE), local tangent space alignment (LTSA), and Laplacian eigenmaps, are investigated with these classifiers. In experiments with Hyperion and AVIRIS hyperspectral data, the proposed SLML-WkNN performed better than ULML- kNN and SLML-k NN, and the highest accuracies were obtained using weights provided by supervised LTSA and LLE.Keywords
This publication has 23 references indexed in Scilit:
- Generalised supervised local tangent space alignment for hyperspectral image classificationElectronics Letters, 2010
- A Nonparametric Feature Extraction and Its Application to Nearest Neighbor Classification for Hyperspectral Image DataIEEE Transactions on Geoscience and Remote Sensing, 2009
- On kernel difference-weighted k-nearest neighbor classificationPattern Analysis and Applications, 2008
- Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE)Published by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Exploiting manifold geometry in hyperspectral imageryIEEE Transactions on Geoscience and Remote Sensing, 2005
- Neighbor-weighted K-nearest neighbor for unbalanced text corpusExpert Systems with Applications, 2005
- Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space AlignmentSIAM Journal on Scientific Computing, 2004
- Laplacian Eigenmaps for Dimensionality Reduction and Data RepresentationNeural Computation, 2003
- Nonlinear Dimensionality Reduction by Locally Linear EmbeddingScience, 2000
- A Global Geometric Framework for Nonlinear Dimensionality ReductionScience, 2000