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

Hyperspectral images (HSIs) have fine spectral information, and rich spatial information, of
which the feature quality is one of the key factors that affect the classification performance …

Spectral features cannot effectively reflect the differences among the ground objects and
distinguish their boundaries in hyperspectral image (HSI) classification. Multi-scale feature …

Feature extraction is of high importance for effective data classification in hyperspectral
imaging (HSI). Considering the high correlation among band images, spectral-domain …

In this paper, we propose a spectral–spatial feature based classification (SSFC) framework
that jointly uses dimension reduction and deep learning techniques for spectral and spatial …

Explosive growth of applications in hyperspectral image (HSI) has made HSI classification a
hot topic in the remote sensing community. The key to improve classification accuracy is how …

Learning representative and discriminative feature that make full use of spectral-spatial
information is of cardinal significance for hyperspectral imagery (HSI) interpretation. In this …

In this paper, a novel multi-scale total variation method is proposed to extract structural
features from hyperspectral images (HSIs), which consists of the following steps. First, the …

In hyperspectral image (HSI) classification, the combination of spectral information and
spatial information can be applied to enhance the classification performance. In order to …

The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral
imagery has led to significant improvements in terms of classification performance. The task …