Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …

Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …

In this paper, we designed an end-to-end spectral-spatial residual network (SSRN) that
takes raw 3-D cubes as input data without feature engineering for hyperspectral image …

A generative adversarial network (GAN) usually contains a generative network and a
discriminative network in competition with each other. The GAN has shown its capability in a …

Over the past few years making use of deep networks, including convolutional neural
networks (CNNs) and recurrent neural networks (RNNs), classifying hyperspectral images …

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 …

Deep learning techniques have been widely applied to hyperspectral image (HSI)
classification and have achieved great success. However, the deep neural network model …

Purpose Oral cancer is a complex wide spread cancer, which has high severity. Using
advanced technology and deep learning algorithm early detection and classification are …

Hyperspectral image (HSI) contains a large number of spatial-spectral information, which
will make the traditional classification methods face an enormous challenge to discriminate …

As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully
Convolutional Network (FCN) model achieved state-of-the-art performance for natural image …