Results 41 to 50 of about 9,466 (207)

Blind Hyperspectral Unmixing with Enhanced 2DTV Regularization Term

Remote Sensing, 2023
For the problem where the existing hyperspectral unmixing methods do not take full advantage of the correlations and differences between all these bands, resulting in affecting the final unmixing results, we design an enhanced 2DTV (E-2DTV ...
Peng Wang, Xun Shen, Yingying Kong, Xiwang Zhang, Liguo Wang   +4 more
doaj   +1 more source

Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm. [PDF]

, 2010
This paper studies a semi-supervised Bayesian unmixing algorithm for hyperspectral images. This algorithm is based on the normal compositional model recently introduced by Eismann and Stein.
Dobigeon, Nicolas, Eches, Olivier, Tourneret, Jean-Yves   +2 more
core   +2 more sources

Pixel-Level and Global Similarity-Based Adversarial Autoencoder Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is a critical task in remote sensing, enabling the decomposition of hyperspectral data into their constituent endmembers and abundances.
Wei Tao, Haiyang Zhang, Shan Zeng, Long Wang, Chaoxian Liu, Bing Li   +5 more
doaj   +1 more source

Implementation strategies for hyperspectral unmixing using Bayesian source separation [PDF]

, 2010
Bayesian Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing.
Dobigeon, Nicolas, Guiheneuf, Mael, Moussaoui, Said, Schmidt, Albrecht, Schmidt, Frederic, Treguier, Erwan   +5 more
core   +6 more sources

Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model

Remote Sensing, 2023
Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by ...
Ying Cheng, Liaoying Zhao, Shuhan Chen, Xiaorun Li   +3 more
doaj   +1 more source

Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images

, 2015
One of the challenges in hyperspectral data analysis is the presence of mixed pixels. Mixed pixels are the result of low spatial resolution of hyperspectral sensors. Spectral unmixing methods decompose a mixed pixel into a set of endmembers and abundance
Ghassemian, Hassan, Rajabi, Roozbeh
core   +1 more source

Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery [PDF]

, 2007
This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive ...
Chein-i Chang, Jean-yves Tourneret, Nicolas Dobigeon, Senior Member, Senior Member   +4 more
core   +6 more sources

Two‐Dimensional Reconfigurable Photodiode for In‐Sensor Color Filtering and Spectral Logic

Advanced Materials, EarlyView.
By harnessing the photodoping of different aggregates, the device exhibits wavelength‐dependent volatile‐to‐nonvolatile photoresponses that can be reconfigured via bias modulation. This enables in‐sensor color filtering and spectral‐encrypted information processing, eliminating reliance on external optical filters or post‐processing algorithms ...
Xiaokun Guo, Yaoqiang Zhou, Yufeng Zhang, Xinyi Zhao, Yue Pang, Lei Tong, Zhipei Sun, Jianbin Xu   +7 more
wiley   +1 more source

Non-convex regularization in remote sensing [PDF]

, 2016
In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high ...
Barlaud, Michel, Flamary, Remi, Tuia, Devis   +2 more
core   +4 more sources

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source