Many spectral matching algorithms, ranging from the traditional clustering techniques to the
recent automated matching models, have evolved. This paper provides a review and up-to …

In comparison with conventional machine learning algorithms, deep learning can effectively
express the deep features of remote sensing images. Considering the rich spectral and …

Abstract Melamine (2, 4, 6-triamino-1, 3, 5-triazine) contamination of food has become an
urgent and broadly recognized topic as a result of several food safety scares in the past five …

In the rapidly evolving realm of remote sensing technology, the classification of
Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent …

The final product of a geologic remote sensing data analysis using multi spectral and
hyperspectral images is a mineral (abundance) map. Multispectral data, such as ASTER …

Over the past decades, maritime accidents have been increasing due to the rise in maritime
transportation and ship traffic. While detecting accident-prone vessels is crucial, it is equally …

The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a
classical hyperspectral classification method, spectral matching (SM) can automatically map …

At present, deep learning is a hot topic in the field of the classification of hyperspectral image
(HSI), and it has aroused wide attention. However, in fine-grained classification tasks, such …

Convolutional neural networks (CNNs) have been prominent in most hyperspectral image
(HSI) processing applications due to their advantages in extracting local information …

We propose a kernel nonlocal joint collaborative representation classification method based
on column generation for hyperspectral imagery. The proposed approach first maps the …