Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …

The remarkable growth of deep learning (DL) algorithms in hyperspectral images (HSIs) in
recent years has garnered a lot of research space. This study examines and analyses over …

Fruit-tree crops generate food and income for local households and contribute to South
Africa's gross domestic product. Timely and accurate phenotyping of fruit-tree crops is …

Hyperspectral image classification has two key characteristics: spatial dependency and
class imbalance. The synthetic oversampling methods for solving the between-class …

Analysing wildfire initiation patterns and identifying their primary drivers is essential for the
development of more efficient fire prevention strategies. However, such analyses have …

Research in the medical field does not stop evolving. This evolution obliges doctors to be up-
to-date in order to well manage every situation that may occur with their patients. However …

Imbalance in hyperspectral images creates a crisis in its analysis and classification
operation. Resampling techniques are utilized to minimize the data imbalance. Although …

The class imbalance problem has been reported to exist in remote sensing and hinders the
classification performance of many machine learning algorithms. Several technologies, such …

Due to the powerful feature expression ability of deep learning and its end-to-end nonlinear
mapping relationship, deep-learning-based methods have become the mainstream method …

Deep learning versus feature engineering has drawn significant attention specifically for
applications where expertly crafted features have been used for decades. Human activity …