Forgery and tampering continue to provide unnecessary economic burdens. Although new
anti-forgery and counterfeiting technologies arise, they inadvertently lead to the …

In deep learning-based hyperspectral remote sensing image classification tasks, random
sampling strategies are typically used to train model parameters for testing and evaluation …

Many studies have recently used several deep learning methods for detecting skin cancer.
However, hyperspectral imaging (HSI) is a noninvasive optics system that can obtain …

Air pollution has emerged as a global problem in recent years. Particularly, particulate
matter (PM2. 5) with a diameter of less than 2.5 μm can move through the air and transfer …

Simple Summary Early esophageal cancer detection is crucial for patient survival; however,
even skilled endoscopists find it challenging to identify the cancer cells in the early stages. In …

Complexity of any onboard hyperspectral image sensor is a challenging issue. The existing
hyperspectral image compression algorithm plays a great role in reducing the data …

The severity of diabetic retinopathy (DR) is directly correlated to changes in both the oxygen
utilization rate of retinal tissue as well as the blood oxygen saturation of both arteries and …

Hyperspectral images (HSIs) have been playing an important role in the field of ground
object classification because of their rich spatial and spectral information. Aiming at how to …

Surface reflectance is an important data product in imaging spectroscopy for obtaining
surface information. The complex retrieval of surface reflectance, however, critically relies on …

The lack of labeled training samples restricts the improvement of Hyperspectral Remote
Sensing Image (HRSI) classification accuracy based on deep learning methods. In order to …