Results 241 to 250 of about 5,351 (266)

Adaptive Compressed Classification for hyperspectral imagery

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
Hyperspectral imaging (HSI) is a useful tool for the classification of vast areas. High accuracy is achieved by means of spectral information for each pixel, which inherently leads to a huge amount of data and, thus, requires costly processing.
Jürgen T. Hahn, Simon Rosenkranz, Abdelhak M. Zoubir   +2 more
openaire   +2 more sources

Estimate the Number of Endmembers for Hyperspectral Imagery

2009 International Conference on Environmental Science and Information Application Technology, 2009
In practice, the determination of the number of endmembers for hyperspectral images of the areas without priori knowledge is highly difficult. This article brings forward an automatic method, which can estimate the number of endmembers for hyperspectral imagery without priori knowledge of the area, according to the theory of Orthogonal subspace ...
Wei Chen, Xu-chu Yu, He Wang
openaire   +1 more source

Anomaly detection and classification for hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing, 2002
Anomaly detection becomes increasingly important in hyperspectral image analysis, since hyperspectral imagers can now uncover many material substances which were previously unresolved by multispectral sensors. Two types of anomaly detection are of interest and considered in this paper. One was previously developed by Reed and Yu to detect targets whose
Chein-I Chang, Shao-Shan Chiang
openaire   +1 more source

Band reduction for hyperspectral imagery processing

SPIE Proceedings, 2010
Feature reduction denotes the group of techniques that reduce high dimensional data to a smaller set of components. In remote sensing feature reduction is a preprocessing step to many algorithms intended as a way to reduce the computational complexity and get a better data representation.
openaire   +2 more sources

Detection of underwater objects in hyperspectral imagery

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016
One of the biggest challenges in detecting underwater objects in hyperspectral imagery is that, unlike the land-based case, the observed spectrum of an underwater target is highly dependent on the properties of the surrounding water, as well as the depth of the target. In this paper we present a very general framework for underwater detection.
openaire   +2 more sources

The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination

Sensors, 2021
Saverio Francini, Gherardo Chirici, Francesca Giannetti   +2 more
exaly  

Anomaly detection and compensation for hyperspectral imagery.

2005
Hyperspectral sensors observe hundreds or thousands of narrow contiguous spectral bands. The use of hyperspectral imagery for remote sensing applications is new and promising, yet the characterization and analysis of such data by exploiting both spectral and spatial information have not been extensively investigated thus far.
openaire   +1 more source

Hyperspectral imagery reveals large spatial variations of heavy metal content in agricultural soil - A case study of remote-sensing inversion based on Orbita Hyperspectral Satellites (OHS) imagery

Journal of Cleaner Production, 2022
Tianyu Xiang, Shiming Wang
exaly  

Prior-Based Tensor Approximation for Anomaly Detection in Hyperspectral Imagery

IEEE Transactions on Neural Networks and Learning Systems, 2022
Qian Du, Lu Li
exaly  

Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Based Hyperspectral Imagery

Remote Sensing, 2022
Qikai Lu, Lifei Wei, Shuang Tian
exaly