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Found 5 papers, 4 papers with code
Superpixelwise Low-rank Approximation based Partial Label Learning for Hyperspectral Image Classification
In this paper, we propose a novel superpixelwise low-rank approximation (LRA)-based partial label learning method, namely SLAP, which is the first to take into account partial label learning in HSI classification.
Personalized Federated Learning with Local Attention
The key challenge of FL is the heterogeneity of local data in different clients, such as heterogeneous label distribution and feature shift, which could lead to significant performance degradation of the learned models.
Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification
By taking advantage of the local manifold structure, a Laplacian graph is constructed from the superpixels to ensure that a typical pixel should be similar to its neighbors within the same superpixel.
Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification
Specifically, by utilizing the local spatial information and incorporating the predictions from a typical classifier, the first module segments pixels of an input HSI (or its restoration generated by the second module) into superpixels.
Hyperspectral Image Classification via Sparse Representation With Incremental Dictionaries
In this letter, we propose a new sparse representation (SR)-based method for hyperspectral image (HSI) classification, namely SR with incremental dictionaries (SRID).
