Recently, the rapid development of deep learning has greatly improved the performance of
image classification. However, a central problem in hyperspectral image (HSI) classification …

Dimensionality reduction (DR) is an important way of improving the classification accuracy of
a hyperspectral image (HSI). Graph learning, which can effectively reveal the intrinsic …

Hyperspectral image (HSI) contains rich spatial and spectral information, which has been
widely used in resource exploration, ecological environment monitoring, land cover …

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 …

Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data
and separate the interclass data, and it is very useful to analyze the high-dimensional data …

The graph embedding (GE) framework is very useful to extract the discriminative features of
hyperspectral images (HSIs) for classification. However, a major challenge of GE is how to …

This paper proposes a new approach based on an unsupervised deep learning (DL) model
for landslide detection. Recently, supervised DL models using convolutional neural …

A hyperspectral image (HSI) has also highly correlated and redundant data, in addition to
abundant spatial and spectral information. Only the spectral characteristics were usually …

The spatial distribution of forest stands is one of the fundamental properties of forests. Timely
and accurately obtained stand distribution can help people better understand, manage, and …

Decision tree classification is one of the most efficient methods for obtaining land use/land
cover (LULC) information from remotely sensed imageries. However, traditional decision …