Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …

As one of state-of-the-art supervised novelty detection models, support vector machine-
supervised novelty detection (SVM-SND) can recognize whether a test instance is a novelty …

Hyperspectral images (HSIs) comprise plenty of information in the spatial and spectral
domain, which is highly beneficial for performing classification tasks in a very accurate way …

Dimensionality reduction (DR) is important for feature extraction and classification of
hyperspectral images (HSIs). Recently proposed superpixel-based DR models have shown …

Remote sensing images have the essential attribute of large-scale spatial variation and
complex scene information, as well as the high similarity between various classes and the …

Background and objectives Compared with traditional RGB images, medical hyperspectral
imagery (HSI) has numerous continuous narrow spectral bands, which can provide rich …

Collaborative Representation (CR) models have been successfully employed for
Hyperspectral Images (HSIs) classification because of the effectiveness and simplicity …

Graph embedding is an effective method that has shown superiority in dimensionality
reduction (DR) for hyperspectral imagery (HSI) due to its ability to characterize the intrinsic …

As the major research in pattern recognition, unsupervised dimension reduction is a
challenging problem because of no label information. Most unsupervised dimension …

Unsupervised hyperspectral image (HSI) band selection methods have been attracting ever-
increasing attention. However, the local structural features captured by most of the existing …