Hyperspectral remote sensing technology has a strong capability for ground object detection
due to the low spatial resolution of hyperspectral imaging spectrometers. A single pixel that …

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 …

Convolutional neural networks are widely used in the field of hyperspectral image
classification because of their excellent nonlinear feature extraction ability. However, as the …

In recent years, learning algorithms based on deep convolution frameworks have gradually
become the research hotspots in hyperspectral image classification tasks. However, in the …

Convolution neural network (CNN) utilizes alternating convolutional and pooling layers to
learn representative spatial information when the training samples are sufficient. However …

高光谱遥感技术具有强大的地物探测能力. 然而, 其空间分辨率低的特点导致光谱图像中存在
大量的混合像元, 该现象阻碍了高光谱技术的应用和发展. 针对米级以下的高光谱图像 …

This paper presents a novel approach for spectral unmixing of remotely sensed
hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the …

Spectral variability in hyperspectral images can result from factors including environmental,
illumination, atmospheric and temporal changes. Its occurrence may lead to the propagation …

In the community of remote sensing, nonlinear mixture models have recently received
particular attention in hyperspectral image processing. In this paper, we present a novel …

Hidden Markov chain (HMC) models have been widely used in unsupervised image
segmentation. In these models, there is a double process; a hidden one noted X and an …