… -fitting problem of the neural network has been alleviated to … the hyperspectral image
information, 1 × 1 convolutional layers … the 1× 1 convolutional layers to analyze the hyperspectral …

… tasks, including the classification of common two-dimensional images. In this paper, …
convolutional neural networks are employed to classify hyperspectral images directly in spectral …

… neural network architecture for spectral-spatial classification of HSI datasets. The proposed
model adds internal connections between the hidden blocks of convolutional … the network. As …

… deep convolutional neural networks (CNNs) have been widely used for the HSI classification…
Plaza, “A new deep convolutional neural network for fast hyperspectral image classification,…

… we propose a novel convolutional neural network framework for the characteristics of
hyperspectral image data called HSI-CNN, which can also provides ideas for the processing of one-…

… training samples in hyperspectral image classification. … in hyperspectral image classification
in Section 2. In Section 3, we present the architecture of the convolutional neural network …

… the classification of hyperspectral images. The proposed CNN is a 3-D network that uses both
spectral … border areas in the image, and has been efficiently implemented using graphics …

Convolutional neural networks (CNNs) have been attracting increasing attention in
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …

… In order to improve the classification … HSI classification, which integrates both active learning
and deep learning into a unified framework. First, we train a convolutional neural network (…

… network where is the number of hyperspectral image bands. is a kernel with a stride of 1 for
the convolutional … is the number of the ouput bands for the convolutional and pooling layer. …