Modern hyperspectral imaging systems produce huge datasets potentially conveying a great
abundance of information; such a resource, however, poses many challenges in the …

Over the past few years, the computer vision domain has evolved and made a revolutionary
transition from human-engineered features to automated features to address challenging …

Recently, deep learning-based algorithms have been widely used for classification of
hyperspectral images (HSIs) by extracting invariant and abstract features. In our conference …

The large spectral variability and nonlinearity of hyperspectral images (HSIs) make
classification a challenging task. Hence, the powerful capacities for feature extraction and …

摘要本文以结合深度学习的遥感影像特征提取和不充足样本下地物识别与分类作为出发点,
对2017—2019 年用于遥感图像处理中小样本训练的深度学习方法进行归类总结 …

With the continuous advancement of deep learning technology, researchers have made
further progress in the hyperspectral image (HSI) classification domain. We propose a …

In this paper, a Capsulenet-based framework is proposed for extracting spectral and spatial
features for improving hyperspectral image classification. Unlike conventional strategies, the …

In recent years, deep learning technology has been widely used in the field of hyperspectral
image classification and achieved good performance. However, deep learning networks …

The capsule network (Caps) is a novel type of neural network that has great potential for the
classification of hyperspectral remote sensing. However, the Caps suffers from the issue of …

Deep learning has been successfully utilized in synthetic aperture radar (SAR) automatic
target recognition tasks and obtained state-of-the-art results. However, current deep learning …