Results 11 to 20 of about 19,235 (216)

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras.
Antonio Plaza, Jocelyn Chanussot, José M. Bioucas-dias, Mario Parente, Nicolas Dobigeon, Paul Gader, Qian Du, Senior Member, Senior Member   +8 more
core   +10 more sources

SPATIAL INTERPOLATION AS A TOOL FOR SPECTRAL UNMIXING OF REMOTELY SENSED IMAGES [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
Super resolution-based spectral unmixing (SRSU) is a recently developed method for spectral unmixing of remotely sensed imagery, but it is too complex to implement for common users who are interested in land cover mapping.
L. Xi, C. Xiaoling
doaj   +1 more source

Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing

Remote Sensing, 2021
Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult ...
Ziqiang Hua, Xiaorun Li, Jianfeng Jiang, Liaoying Zhao   +3 more
doaj   +1 more source

DLR HySU—A Benchmark Dataset for Spectral Unmixing

Remote Sensing, 2021
Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers.
Daniele Cerra, Miguel Pato, Kevin Alonso, Claas Köhler, Mathias Schneider, Raquel de los Reyes, Emiliano Carmona, Rudolf Richter, Franz Kurz, Peter Reinartz, Rupert Müller   +10 more
doaj   +1 more source

Superpixel-Based Weighted Sparse Regression and Spectral Similarity Constrained for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
With the support of spectral libraries, sparse unmixing techniques have gradually developed. However, some existing sparse unmixing algorithms suffer from problems, such as insufficient utilization of spatial information and sensitivity to noise.
Yao Liang, Hengyi Zheng, Guoguo Yang, Qian Du, Hongjun Su   +4 more
doaj   +1 more source

An Efficient Attention-Based Convolutional Neural Network That Reduces the Effects of Spectral Variability for Hyperspectral Unmixing

Applied Sciences, 2022
The purpose of hyperspectral unmixing (HU) is to obtain the spectral features of materials (endmembers) and their proportion (abundance) in a hyperspectral image (HSI).
Baohua Jin, Yunfei Zhu, Wei Huang, Qiqiang Chen, Sijia Li   +4 more
doaj   +1 more source

USE SATELLITE IMAGES AND IMPROVE THE ACCURACY OF HYPERSPECTRAL IMAGE WITH THE CLASSIFICATION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
The best technique to extract information from remotely sensed image is classification. The problem of traditional classification methods is that each pixel is assigned to a single class by presuming all pixels within the image.
P. Javadi
doaj   +1 more source

A Global Spectral–Spatial Feature Learning Network for Semisupervised Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Neural networks have been widely applied in hyperspectral unmixing in the past few years. However, most networks only focus on extracting the spectral information or local spectral–spatial correlation of a single pixel. In order to further explore
Fanqiang Kong, Mengyue Chen, Yunsong Li, Dan Li   +3 more
doaj   +1 more source

HYPERSPECTRAL IMAGE RESOLUTION ENHANCEMENT BASED ON SPECTRAL UNMIXING AND INFORMATION FUSION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be
J. Bieniarz, D. Cerra, J. Avbelj, P. Reinartz, R. Müller   +4 more
doaj   +1 more source

Context Dependent Spectral Unmixing

2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012
A hyperspectral unmixing algorithm that finds multiple sets of endmembers is introduced. The algorithm, called Context Dependent Spectral Unmixing (CDSU), is a local approach that adapts the unmixing to different regions of the spectral space. It is based on a novel objective function that combines context identification and unmixing into a joint ...
Hamdi Jenzri, Hichem Frigui, Paul Gader
openaire   +3 more sources