Unsupervised Classification

A Fast and Accurate Similarity-constrained Subspace Clustering Algorithm for Hyperspectral Image

In this paper, we propose a fast algorithm that obtains a sparse representation coefficient matrix by first selecting a small set of pixels that best represent their neighborhood. Then, it performs spatial filtering to enforce the connectivity of neighboring pixels and uses fast spectral clustering to get the final clustering map.

Efficient subspace clustering of hyperspectral images using similarity-constrained sampling

The unsupervised classification of hyperspectral images (HSIs) draws attention in the remote sensing community due to its inherent complexity and the lack of labeled data. Among unsupervised methods, sparse subspace clustering (SSC) achieves high …

Hyperspectral image segmentation using 3D regularized subspace clustering model

The accurate segmentation of remotely sensed hyperspectral images has widespread attention in the Earth observation and remote sensing communities. In the past decade, most of the efforts focus on the development of different supervised methods for …

Coded aperture design for compressive spectral subspace clustering

This paper proposes to design a set of coding patterns such that inter-class and intra-class data structure is preserved after the CSI acquisition in order to improve clustering results directly on the compressed domain. To validate the coding pattern design, an algorithm based on sparse subspace clustering (SSC) is proposed to perform clustering on the compressed measurements.