The Levenshtein distance is approximated by the squared Euclidean distance between the embedding vectors, which is fast calculated and clustering algorithm friendly.
Recent advances in neural networks have made great progress in the hyperspectral image (HSI) classification.
To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectral information integration and the utilization of neural networks.
Ranked #8 on Hyperspectral Image Classification on Indian Pines (Overall Accuracy metric)
At the testing stage, by applying the discriminant model to the pixel-pairs generated by the test pixel and its neighbors, the local structure is estimated and represented as a customized convolutional kernel.
In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery.