Sparse subspace clustering (SSC) has emerged as an effective approach for the automatic
analysis of hyperspectral images (HSI). Traditional SSC-based approaches employ the input …

Hyperspectral remote sensing organically combines traditional space imaging with
advanced spectral measurement technologies, delivering advantages stemming from …

Hyperspectral images (HSIs), captured by different Earth observation airborne and space-
borne systems, provide rich spectral information in hundreds of bands, enabling far better …

Noisy interference and high dimensionality are the main challenges in image classification.
In this paper, a robust low-rank representation via residual projection is proposed. Different …

Clustering algorithms play an essential and unique role in classification tasks, especially
when annotated data are unavailable or very scarce. Current clustering approaches in …

Supervised hyperspectral image (HSI) classification relies on accurate label information.
However, it is not always possible to collect perfectly accurate labels for training samples …

We investigated nearest-neighbor density-based clustering for hyperspectral image
analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) …

In this article, we integrate the spatial-spectral information of hyperspectral image (HSI)
samples into nonnegative matrix factorization (NMF) for affinity matrix learning to address …

Subspace clustering has achieved remarkable performance for hyperspectral image (HSI).
However, existing methods are often computationally expensive and have limited ability to …

Deep subspace clustering is an effective method for clustering high-dimensional data, and it
provides state-of-the-art results in clustering hyperspectral images (HSI). However, these …