12 accepted papers
Cross-scene hyperspectral image (HSI) recognition aims to assign a unique label to each pixel in the target scene by transferring knowledge from the source scene. Existing methods primarily rely on fully labeled source data and either partially labeled or unlabeled target data. No prior work has add
Graph-based incomplete multi-view clustering algorithms have gathered much attention due to their impressive clustering performance. However, existing methods primarily leverage intra-view correlation from observed views, while ignoring the exploration of explicit compensation relationships between
Multi-view clustering of remote sensing data presents significant challenges, as it integrates diverse data representations to improve Earth observation. Although existing anchor graph-based methods have yielded promising results, they generally exhibit two key limitations: (1) the time-consuming pr
Multi-view clustering for remote sensing data has received increasing attention by leveraging diverse data representations to enhance Earth observation. Existing methods are primarily developed under the assumption that each pixel is fully observed across all views. No prior work has investigated th
Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face several limitations: (1) Recovered data may be unsuitable for spe…
Anchor-based incomplete multi-view clustering methods utilize anchors to uncover clustering structures. However, relying on anchor graphs for producing final indicators is indirect, which can lead to information loss and suboptimal outcomes. Besides, most methods neglect the potential of anchors for
Pansharpening fuses lower-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images to generate high-resolution multispectral (HRMS) images that preserves both spatial and spectral information. Most deep pansharpening methods face challenges in cross-modal feature extract
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) classification aims at assigning a unique label for every pixel to identify categories of different land covers. Existing deep learning models for HSIs are usually performed in a traditional learning paradigm. Being emerging machines, quantum computers are limited in the no…
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…