Current hyperspectral image classification assumes that a predefined classification system
is closed and complete, and there are no unknown or novel classes in the unseen data …

Hyperspectral image classification (HSIC) methods based on convolutional neural network
(CNN) continue to progress in recent years. However, high complexity, information …

Hyperspectral image (HSI) classification is a most challenging task in hyperspectral remote
sensing field due to unique characteristics of HSI data. It consists of huge number of bands …

To improve the accuracy and generalization ability of hyperspectral image classification, a
feature extraction method integrating principal component analysis (PCA) and local binary …

Convolutional neural networks (CNN) have led to a successful breakthrough for
hyperspectral image classification (HSIC). Due to the intrinsic spatial-spectral specificities of …

Wealthy spectral information provided by hyperspectral image (HSI) offers great benefits for
many applications in hyperspectral data exploitation. However, processing such high …

While the recurrent neural network (RNN) has achieved remarkable performance on
dynamic and control tasks, its applications to image processing, particularly target detection …

Hyperspectral image (HSI) provided with a substantial number of correlated bands causes
calculation consumption and an undesirable “dimension disaster” problem for the …

Due to significant inter-band correlation resulting from the use of hundreds of contiguous
spectral bands, band selection (BS) is commonly used to reduce data dimensionality for …

In recent years, due to its powerful feature extraction ability, the deep learning method has
been widely used in hyperspectral image classification tasks. However, the features …