In recent years, subspace clustering has become increasingly popular and achieved great
success in band selection (BS) of hyperspectral imagery. However, current subspace …

Extracting spectral-spatial features from Hyperspectral imagery (HSI) has been proven to be
efficient for classification tasks. A recently developed superpixelwised PCA (SuperPCA) …

Information fusion plays a vital role in hyperspectral band selection as it enables the
exploration of the spatial-spectral structure relationship present in bands of hyperspectral …

Hyperspectral imagery from drones are excellent sources of high-resolution plant-level data
that helps in efficient crop identification for smart agriculture practices. However, the plant …

Medical hyperspectral imaging present a promising avenue for non-invasive diagnostic
methods for diseases. Nonetheless, the sparsity of medical hyperspectral data within high …

Change detection (CD) aims to identify differences in the same scene at different times. With
the increasing amount of hyperspectral images (HSIs), more and more CD techniques use …

Hyperspectral imagery has a high-dimensional curse due to numerous spectral bands. Band
selection (BS) is crucial for efficiently reducing dimensionality, retaining only essential bands …

Detecting sea fog through remote sensing imagery is crucial for ensuring maritime safety
and efficient navigation. This study introduces an innovative sea fog detection method …

Band selection is crucial in spectral imaging, as it involves choosing the most relevant bands
from large hyperspectral datasets to retain essential information while reducing the burden …