Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification

摘要

Approaches to combine local manifold learning (LML) and the $k$ -nearest-neighbor ($k$NN) classifier are investigated for hyperspectral image classification. Based on supervised LML (SLML) and $k$ NN, a new SLML-weighted $k$NN (SLML-W $k$NN) 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 $k$NN (ULML- $k$NN 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-W$k$NN performed better than ULML- $k$NN and SLML-$k$ NN, and the highest accuracies were obtained using weights provided by supervised LTSA and LLE.