Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of
both labeled and unlabelled data. An essential class of SSL methods, referred to as graph …

Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

Hyperspectral images (HSIs) provide rich spectral–spatial information with stacked
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …

The classification of hyperspectral image is a challenging task due to the high dimensional
space, with large number of spectral bands, and low number of labeled training samples. To …

Most traditional algorithms for compressive sensing image reconstruction suffer from the
intensive computation. Recently, deep learning-based reconstruction algorithms have been …

Deep convolutional neural networks have been dominating in the field of hyperspectral
image (HSI) classification. However, single convolutional kernel can limit the receptive field …

In recent years, supervised learning has been widely used in various tasks of optical remote
sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation …

Hyperspectral anomaly detection (HAD) is a research endeavor of high practical relevance
within remote sensing scene interpretation. In this work, we propose an unsupervised …

The hyperspectral images are composed of a variety of textures across the different bands
which increase the spectral similarity and make it difficult to predict the pixel-wise labels …

Hyperspectral Image classification plays an important role in the maintenance of remote
image analysis, which has been attracting a lot of research interest. Although various …