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 …

In the last five years, deep learning has been introduced to tackle the hyperspectral image
(HSI) classification and demonstrated good performance. In particular, the convolutional …

Convolutional neural networks (CNNs) exhibit good performance in image processing tasks,
pointing themselves as the current state-of-the-art of deep learning methods. However, the …

With the rapid development of aerospace and various remote sensing platforms, the amount
of data related to remote sensing is increasing rapidly. To meet the application requirements …

Due to its remarkable feature representation capability and high performance, convolutional
neural networks (CNN) have emerged as a popular choice for hyperspectral image (HSI) …

Deep learning (DL) has proven to be a promising technique for hyperspectral image (HSI)
classification. However, due to complex network structure and massive parameters, it is …

This article proposes a novel hierarchical residual network with attention mechanism
(HResNetAM) for hyperspectral image (HSI) spectral-spatial classification to improve the …

Convolutional neural network (CNN) has been successfully introduced to hyperspectral
image (HSI) classification and achieved effective performance. With the depth of the CNN …

In recent years, image classification on hyperspectral imagery utilizing deep learning
algorithms has attained good results. Thus, spurred by that finding and to further improve the …

The development of remote sensing images in recent years has made it possible to identify
materials in inaccessible environments and study natural materials on a large scale. But …