Results 71 to 80 of about 9,466 (207)

Superpixel-Guided Matrix-Valued Kernel Functions for Multiscale Nonlinear Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is a critical challenge in the analysis of hyperspectral remote sensing data. Due to the complex interactions between incident light and materials, which are significantly influenced by the three-dimensional geometry of the scene ...
Xiu Zhao, Meiping Song
doaj   +1 more source

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Remote Sensing, 2017
Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional ...
Ruyi Feng, Yanfei Zhong, Lizhe Wang, Wenjuan Lin   +3 more
doaj   +1 more source

A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for
Qiqi Zhu, Linlin Wang, Wen Zeng, Qingfeng Guan, Zhen Hu   +4 more
doaj   +1 more source

Approximate Sparse Regularized Hyperspectral Unmixing [PDF]

Mathematical Problems in Engineering, 2014
Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing ...
Chengzhi Deng, Yaning Zhang, Shengqian Wang, Shaoquan Zhang, Wei Tian, Zhaoming Wu, Saifeng Hu   +6 more
openaire   +1 more source

Gradient Type Methods for Linear Hyperspectral Unmixing

CSIAM Transactions on Applied Mathematics, 2022
Summary: Hyperspectral unmixing (HU) plays an important role in terrain classification, agricultural monitoring, mineral recognition and quantification, and military surveillance. The existing model of the linear HU requires the observed vector to be a linear combination of the vertices.
Xu, Fangfang, Wang, Yating, Li, Yanyan, Liu, Lu, Tian, Tonghua   +4 more
openaire   +3 more sources

A Comprehensive Review of Traditional and Machine Learning‐Assisted Diffuse Optical Spectroscopy and Imaging Techniques for Dermatological Applications

Advanced Photonics Research, Volume 7, Issue 5, May 2026.
This review critically examines clinical studies on both conventional and machine learning (ML)‐integrated diffuse optical spectroscopy and imaging methods for dermatological applications, with a primary focus on the past decade and inclusion of earlier foundational work where appropriate.
Iftak Hussain, Li Xiuting, Randall Ang Jie, Keertana Vinod Ram, Mark Teo Ju Teng, Valerie Xinhui Teo, Malini Olivo, U. S. Dinish   +7 more
wiley   +1 more source

Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation

, 2016
The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA provides a model for a hyperspectral image analysis that accounts for spectral variability and ...
Zare, Alina, Zou, Sheng
core   +1 more source

Deep Learning Integration in Optical Microscopy: Advancements and Applications

Microscopy Research and Technique, Volume 89, Issue 5, Page 791-814, May 2026.
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel, Budheswar Dehury, Nirmal Mazumder   +5 more
wiley   +1 more source

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

Remote Sensing, 2019
Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression.
Ruyi Feng, Lizhe Wang, Yanfei Zhong
doaj   +1 more source

Conventional to Deep Learning Methods for Hyperspectral Unmixing: A Review

Remote Sensing
Hyperspectral images often contain many mixed pixels, primarily resulting from their inherent complexity and low spatial resolution. To enhance surface classification and improve sub-pixel target detection accuracy, hyperspectral unmixing technology has ...
Jinlin Zou, Hongwei Qu, Peng Zhang
doaj   +1 more source