The globe's population is increasing day by day, which causes the severe problem of
organic food for everyone. Farmers are becoming progressively conscious of the need to …

The representation power of convolutional neural network (CNN) models for hyperspectral
image (HSI) analysis is in practice limited by the available amount of the labeled samples …

Classification in hyperspectral remote sensing images (HRSIs) is a challenging process in
image analysis and one of the most popular topics. In recent years, many methods have …

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

Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of
information for the entire remote sensing community, thanks to their unprecedented …

Remotely sensed spectral diversity has emerged as a promising proxy for plant diversity.
However, spectral diversity approaches relate image spectra to plant community diversity by …

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 fusion of a single panchromatic (PAN) band with a lower resolution multispectral (MS)
image to raise the MS resolution to that of the PAN is known as pansharpening. In the last …

In a context of accelerated human-induced biodiversity loss, remote sensing (RS) is
emerging as a promising tool to map plant biodiversity from space. Proposed approaches …

Due to the inconsistent imaging environment, the styles of multitemporal multispectral
images (MSIs) are quite different, such as image brightness and transparency. For …