Hyperspectral image classification has been a vibrant area of research in recent years.
Given a set of observations, ie, pixel vectors in a hyperspectral image, classification …

Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral
data analysis. Convolutional neural networks (CNN) have been introduced to HSI …

Background Computer-aided diagnosis (CAD) can assist physicians in effective and efficient
diagnostic decision-making. CAD systems are currently essential tools in some areas of …

In this paper, we propose a novel ensemble and robust anomaly detection method based on
collaborative representation-based detector. The focused pixels used to estimate the …

Hyperspectral remote sensing image unsupervised classification, which assigns each pixel
of the image into a certain land-cover class without any training samples, plays an important …

This paper proposes a simple and effective spatial-spectral (SESS) method for map**
large-scale coastal wetlands using China ZY1-02D satellite hyperspectral data. First, the …

Influence maximization in social networks aims to find a small group of individuals, which
have maximal influence cascades. In this study, an optimization model based on a local …

Currently, long-range spectral and spatial dependencies have been widely demonstrated to
be essential for hyperspectral image (HSI) classification. Due to the transformer superior …

Spectral-spatial classification of remotely sensed hyperspectral images has attracted a lot of
attention in recent years. Although Gabor filtering has been used for feature extraction from …

Recently, low-rank representation (LRR) based hyperspectral image (HSI) restoration
method has been proven to be a powerful tool for simultaneously removing different types of …