Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …

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

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data
redundancy and improve the performance of hyperspectral image (HSI) classification. A …

Singular spectral analysis (SSA) has recently been successfully applied to feature extraction
in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D …

Band selection (BS) is a commonly used dimension reduction technique for hyperspectral
images. In this article, we propose a similarity-based ranking (SR) strategy inspired by a …

Recent years have witnessed the superiority of deep learning-based algorithms in the field
of HSI classification. However, a prerequisite for the favorable performance of these …

As an effective manner to reduce data redundancy and processing inconvenience,
hyperspectral band selection aims to select a subset of informative and discriminative bands …

Band selection is an effective method to deal with the difficulties in image transmission,
storage, and processing caused by redundant and noisy bands in hyperspectral images …

Deep learning-based methods have shown promising outcomes in many fields. However,
the performance gain is always limited to a large extent in classifying hyperspectral image …

As an important topic in hyperspectral image (HSI) analysis, band selection has attracted
increasing attention in the last two decades for dimensionality reduction in HSI. With the …