Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …

Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …

Neural networks have dominated the research of hyperspectral image classification,
attributing to the feature learning capacity of convolution operations. However, the fixed …

随着模式识别, 机器学习, 遥感技术等相关学科领域的发展, 高光谱遥感影像分类研究取得快速
进展. 本文系统总结和评述了当前高光谱遥感影像分类的相关研究进展, 在总结分类策略的基础 …

Recently, the graph convolutional network (GCN) has drawn increasing attention in the
hyperspectral image (HSI) classification. Compared with the convolutional neural network …

With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult
Problem in current research. The graph neural network (GNN) has emerged as an approach …

Deep learning has achieved great successes in conventional computer vision tasks. In this
paper, we exploit deep learning techniques to address the hyperspectral image …

Object detection in optical remote sensing images, being a fundamental but challenging
problem in the field of aerial and satellite image analysis, plays an important role for a wide …

Hyperspectral images (HSIs) have a cube form containing spatial information in two
dimensions and rich spectral information in the third one. The high volume of spectral bands …

In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …