Feature selection (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …

The scene classification approaches using deep learning have been the subject of much
attention for remote sensing imagery. However, most deep learning networks have been …

Hyperspectral image (HSI) consists of hundreds of continuous narrowbands with high
spectral correlation, which would lead to the so-called Hughes phenomenon and the high …

Agriculture applications rely on accurate land monitoring, especially paddy areas, for timely
food security control and support actions. However, traditional monitoring requires field …

As the hyperspectral image consists of hundreds of highly correlated spectral bands, the
selection of informative and highly discriminative bands is necessary for hyperspectral …

With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose
bands based on band correlation and information has become more significant, but also …

Hyperspectral imaging has become a mature technology which brings exciting possibilities
in various domains, including satellite image analysis. However, the high dimensionality and …

Hyperspectral image (HSI) consists of hundreds of contiguous spectral bands, which can be
used in the classification of different objects on earth. The inclusion of both spectral and as …

Hyperspectral band selection is one of the efficacious ways to diminish the size of
hyperspectral images. The process of selecting a few useful bands will be successful when …

As the spectral dimension of hyperspectral images increases, band selection becomes more
and more important when using hyperspectral data. Evolutionary algorithms have been …