Hyperspectral images (HSIs) are known for their high dimensionality and wide spectral
bands that increase redundant information and complicate classification. Outliers and mixed …

Recent improvements in remote sensing techniques (RST) allow for collecting hyperspectral
images (HSIs) with enhanced spatial and spectral resolution. These enhancements boost …

In Hyperspectral images (HSI), dimensionality reduction methods (DRM) play a critical role
in reducing the input data dimension and complexity. As much as the deep learning …

In the era of climate change, monitoring and effective retrieval of soil, water bodies,
vegetation parameters etc. are of utmost importance which is successfully being executed …

An airborne hyperspectral imaging system is typically equipped on an aircraft or unmanned
aerial vehicle (UAV) to capture ground scenes from an overlooking perspective. Due to the …

It has been proved that hyperspectral image (HSI) classification task benefits from
introducing additional spatial information. However, how to classify high-dimensional …

Hyperspectral anomaly detection (HAD) is an important technique used to identify objects
with spectral irregularity that can contribute to object-based image analysis. Latterly …

Hyperspectral image (HSI) classification has continued to be a hot research topic in recent
years, and the broad learning system (BLS) has been considered by scholars for the …

The accurate and timely monitoring of land cover types is of great significance for the
scientific planning, rational utilization, effective protection and management of land …

Hyperspectral images (HSIs) are typically utilized in a wide variety of practical applications.
HSI is replete with spatial and spectral information, which provides precise data for material …