4 accepted papers
Hyperspectral anomaly detection (HAD) aims to discern the objects deviated dramatically from their surrounding pixels. Some deep learning-based models integrating with the low-rank representation (LRR) have been proposed recently. The process of constructing dictionary in these methods is complex an…
Hyperspectral image (HSI) clustering seeks to assign each pixel to a specific class without trained labels. This is a challenging task owing to the spatial and spectral complexity. Recently, anchor graph-based clustering has attracted considerable attention due to its flexibility in handling large-s…
Hyperspectral band selection has been proved to be effective in reducing redundant information for hyperspectral images (HSIs). Most existing band selection methods simply consider the relationship between bands by reshaping them into vectors and destroying the spatial structure. Moreover, the conve…
Hyperspectral band selection aims to identify an optimal sub-set of bands from hyperspectral images (HSIs). Most existing methods explore the relationships between pair-wise pixels in a fixed graph. However, the quality of the initial fixed graph may be influenced by noises and user-defined paramete…