This study presents multidimensional deep learning approaches on hyperspectral images.
Storing, processing and interpreting hyperspectral data is very difficult due to its complexity …

Hyperspectral images provide rich spectral information, which can identifymore objects that
are difficult to recognize in other remote sensing images. Therefore, hyperspectral imaging …

In this paper, we proposed a multi-scale convolutional neural network for hyperspectral
image classification task. Firstly, compared with conventional convolution, we utilize multi …

In recent years, deep learning methods have gradually come to be used in hyperspectral
imaging domains. Because of the peculiarity of hyperspectral imaging, a mass of information …

Recently, convolutional neural networks have demonstrated excellent performance on
various visual tasks, including the classification of common two-dimensional images. In this …

As a powerful visual model, convolutional neural networks (CNNs) have demonstrated
remarkable performance in various visual recognition problems, and attracted considerable …

In recent years, remote sensing and other applications have used hyperspectral image
processing in a variety of ways. For more precise and in-depth information extraction …

卷积神经网络等深度学习模型已经在高光谱影像分类任务中取得了理想的结果. 然而,
由于传统神经元只能进行标量计算, 现有的深度学习模型无法对高光谱影像特征的实例化参数 …

Recently, convolutional neural networks have demonstrated excellent performance on
various visual tasks, including the classification of common two‐dimensional images. In this …

Classification is a principle technique in hyperspectral images (HSIs), where a label is
assigned to each pixel based on its characteristics. However, due to lack of labelled training …