In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …

Multi-view projection methods have demonstrated their ability to reach state-of-the-art
performance on 3D shape recognition. Those methods learn different ways to aggregate …

The goal of few-shot learning is to learn a classifier that generalizes well even when trained
with a limited number of training instances per class. The recently introduced meta-learning …

Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

Object detection in optical remote sensing images, being a fundamental but challenging
problem in the field of aerial and satellite image analysis, plays an important role for a wide …

In this paper, we propose a spectral–spatial feature based classification (SSFC) framework
that jointly uses dimension reduction and deep learning techniques for spectral and spatial …

Dimensionality reduction (DR) is an important way of improving the classification accuracy of
a hyperspectral image (HSI). Graph learning, which can effectively reveal the intrinsic …