Remote sensing hyperspectral imaging can obtain abundant spectral information, which
provides the possibility for the analysis of high precision terrain. The hyperspectral image …

The traditional hyperspectral image classification model only considers the spectral feature
information, and ignores the important role of image spatial structure information in …

In recent years, convolutional neural network has been widely used in the field of computer
vision and achieved good results in image classification. In the field of remote sensing …

Deep learning based methods have recently been successfully explored in hyperspectral
image classification field. However, training a deep learning model still requires a large …

In response to the problems of low accuracy and long time consumption in traditional
hyperspectral remote sensing image classification methods, this paper proposes a …

Hyperspectral image (HSI) is usually composed of hundreds of bands which contain very
rich spatial and spectral information. However, the high-dimensional data may lead to the …

Abstract 3D-2D-1D convolutional neural network Deep learning Hyperspectral image
classification Remote sensing Geomatic engineering not elsewhere classified Objective …

Hyperspectral image classification is an important task in remote sensing image analysis.
Traditional machine learning techniques are difficult to deal with hyperspectral images …

In recent years, deep learning methods based on the convolutional neural network (CNNs)
have demonstrated good performance for hyperspectral image classification (HSI) …

Hyperspectral image is well-known for the identification of the objects on the earth's surface.
Most of the classifier uses the spectral features and does not consider the spatial features to …