Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …

Most machine learning tasks can be categorized into classification or regression problems.
Regression and classification models are normally used to extract useful geographic …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …

Abstract Machine learning (ML) models are a leading analytical technique used to monitor,
map and quantify land use and land cover (LULC) and its change over time. Models such as …

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-
Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost …

Recently, the rapid development of deep learning has greatly improved the performance of
image classification. However, a central problem in hyperspectral image (HSI) classification …

The lack of labels is one of the major challenges in hyperspectral image (HSI) classification.
Widely used Deep Learning (DL) models such as convolutional neural networks (CNNs) …

Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been
recently proposed for feature extraction in hyperspectral remote sensing. With the help of …

In hyperspectral image (HSI) classification, spatial context has demonstrated its significance
in achieving promising performance. However, conventional spatial context-based methods …

With the rapid development of spectral imaging techniques, classification of hyperspectral
images (HSIs) has attracted great attention in various applications such as land survey and …