Results 61 to 70 of about 1,956 (163)

Compact Spectral Imaging: A Review of Miniaturized and Integrated Systems

Laser &Photonics Reviews, Volume 19, Issue 21, November 6, 2025.
This review explores the rapid shift toward compact spectral imaging systems by examining four key design paradigms: Do‐It‐Yourself (DIY) platforms, freeform optics, filter‐on‐chip integration, and multifunctional metasurfaces. The discussion highlights critical applications in medicine, agriculture, and environmental monitoring, providing comparative ...
Sani Mukhtar, Amir Arbabi, Jaime Viegas
wiley   +1 more source

Endmember-Free Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Unmixing networks for hyperspectral images (HSIs) often need to be redesigned for each sensor and initialized with endmember-estimation algorithms, which limits cross-scene generalization.
Baisen Liu, Longfei Hou, Weiming Zheng, Nazik Alturki, Jamel Baili, Weili Kong, Jiaming Pei   +6 more
doaj   +1 more source

An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures

IEEE Access, 2019
Effective hyperspectral unmixing (HU) is essential to the estimation of the underlying materials' signatures (endmember signatures) and their spatial distributions (abundance maps) from a given image (data) of a hyperspectral scene.
Yao-Rong Syu, Chia-Hsiang Lin, Chong-Yung Chi   +2 more
doaj   +1 more source

Fast and Structured Block-Term Tensor Decomposition for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
The block-term tensor decomposition model with multilinear rank-$(L_{r},L_{r},1)$ terms (or the “${\mathsf{LL1}}$ tensor decomposition” in short) offers a valuable alternative formulation for hyperspectral unmixing (HU), which ensures the ...
Meng Ding, Xiao Fu, Xi-Le Zhao
doaj   +1 more source

CCTV‐Hyperspectral Imaging for Suspended Sediment Transport (HISST): Proof‐of‐Concept for a Continuous Day‐and‐Night Monitoring Approach

Water Resources Research, Volume 61, Issue 10, October 2025.
Abstract Effective sediment monitoring is crucial for managing dynamic river environments where suspended sediment transport varies over time. However, manual sampling and turbidity sensor‐based methods provide limited spatial coverage and can be labor‐intensive.
Siyoon Kwon, Hyoseob Noh, Il Won Seo, Yun Ho Lee   +3 more
wiley   +1 more source

SSF-Net: A Spatial–Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep learning has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities.
Bin Wang, Huizheng Yao, Dongmei Song, Jie Zhang, Han Gao   +4 more
doaj   +1 more source

Hybrid spectral unmixing : using artificial neural networks for linear/non-linear switching [PDF]

, 2017
Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing ...
Asmau Ahmed, Chang, Giorgio, Giorgio, Iordache, Li, Mike Smith, Olga Duran, Ripley, Shi, Sohn, Thouvenin, Tuzel, Weeks, Yahya Zweiri   +14 more
core   +2 more sources

Exploring a Hybrid Convolutional Framework for Camouflage Target Classification in Land‐Based Hyperspectral Images

CAAI Transactions on Intelligence Technology, Volume 10, Issue 5, Page 1559-1572, October 2025.
ABSTRACT In recent years, camouflage technology has evolved from single‐spectral‐band applications to multifunctional and multispectral implementations. Hyperspectral imaging has emerged as a powerful technique for target identification due to its capacity to capture both spectral and spatial information.
Jiale Zhao, Dan Fang, Jianghu Deng, Jiaju Ying, Yudan Chen, Guanglong Wang, Bing Zhou   +6 more
wiley   +1 more source

Robust Linear Spectral Unmixing using Anomaly Detection

, 2015
This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by ...
Altmann, Yoann, Hero, Alfred, McLaughlin, Steve   +2 more
core   +1 more source

Unmixing-Guided Convolutional Transformer for Spectral Reconstruction

Remote Sensing, 2023
Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design.
Shiyao Duan, Jiaojiao Li, Rui Song, Yunsong Li, Qian Du   +4 more
doaj   +1 more source