Results 61 to 70 of about 828,208 (170)

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

SCSU–GDO: Superpixel Collaborative Sparse Unmixing with Graph Differential Operator for Hyperspectral Imagery

Remote Sensing
In recent years, remarkable advancements have been achieved in hyperspectral unmixing (HU). Sparse unmixing, in which models mix pixels as linear combinations of endmembers and their corresponding fractional abundances, has become a dominant paradigm in ...
Kaijun Yang, Zhixin Zhao, Qishen Yang, Ruyi Feng   +3 more
doaj   +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

A comparative study of hyperspectral unmixing using different algorithm approaches [PDF]

, 2020
Hyperspectral unmixing (HU) is an important technique for remotely sensed hyperspectral data exploitation. Hyperspectral unmixing is required to get an accurate estimation due to low spatial resolution of hyperspectral cameras, microscopic material ...
Majid Darsono, Abdul, Manap, Redzuan Abdul, Mohd Yusof, Ain Zat, Abdul Manap, Redzuan, Yusof, Ain Zat Mohd, Darsono, Abd Majid   +5 more
core   +1 more source

Multiple graph regularized NMF for hyperspectral unmixing [PDF]

, 2015
Hyperspectral unmixing is an important technique for estimating fraction of different land covers from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) methods with various constraints have been introduced into ...
Lei Tong, Jun Zhou, Zhou, Jun, Qian, Yuntao, Yuntao Qian, Tong, Lei, Yongsheng Gao, Gao, Yongsheng   +7 more
core   +1 more source

Monitoring and Modeling the Soil‐Plant System Toward Understanding Soil Health

Reviews of Geophysics, Volume 63, Issue 1, March 2025.
Abstract The soil health assessment has evolved from focusing primarily on agricultural productivity to an integrated evaluation of soil biota and biotic processes that impact soil properties. Consequently, soil health assessment has shifted from a predominantly physicochemical approach to incorporating ecological, biological and molecular microbiology
Yijian Zeng, Anne Verhoef, Harry Vereecken, Eyal Ben‐Dor, Tom Veldkamp, Liz Shaw, Martine Van Der Ploeg, Yunfei Wang, Zhongbo Su   +8 more
wiley   +1 more source

Mamba-based spatial-spectral fusion network for hyperspectral unmixing

Journal of King Saud University: Computer and Information Sciences
Hyperspectral unmixing (HU) is a critical technique in hyperspectral image (HSI) analysis, aimed at decomposing mixed pixels into a set of spectral signatures (endmembers) and their corresponding abundance values.
Yuquan Gan, Jingtao Wei, Mengmeng Xu
doaj   +1 more source

Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

CAAI Transactions on Intelligence Technology, Volume 10, Issue 1, Page 72-93, February 2025.
Abstract Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum. However, due to sensor limitations and imperfections during the image acquisition and transmission phases, noise is introduced into the acquired image, which can have a negative impact ...
Hazique Aetesam, Suman Kumar Maji, V. B. Surya Prasath   +2 more
wiley   +1 more source

When geoscience meets generative AI and large language models: Foundations, trends, and future challenges

Expert Systems, Volume 41, Issue 10, October 2024.
Abstract Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance.
Abdenour Hadid, Tanujit Chakraborty, Daniel Busby   +2 more
wiley   +1 more source

DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The pervasive presence of mixed pixels in hyperspectral remote sensing imagery poses a substantial constraint on the quantitative progress of remote sensing technology. Hyperspectral unmixing (HU) techniques serve as effective means to address this issue.
Hao Chen, Tao Chen, Yuxiang Zhang, Bo Du, Antonio Plaza   +4 more
doaj   +1 more source