Results 41 to 50 of about 151 (115)

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

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

An Unmixing-Based Network for Underwater Target Detection From Hyperspectral Imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Detecting underwater targets from hyperspectral imagery makes a profound impact on marine exploration. Available methods mainly tackle this problem by modifying the land-based detection algorithms with classical bathymetric models, which usually fail to ...
Jiahao Qi, Zhiqiang Gong, Wei Xue, Xingyue Liu, Aihuan Yao, Ping Zhong   +5 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

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

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

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

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

Efficient Weighted-Adaptive Sparse Constrained Nonnegative Tensor Factorization for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Hyperspectral unmixing aims to separate pure materials and their corresponding proportions that constitute the mixed pixels of hyperspectral imagery (HSI). Recently, the matrix-vector nonnegative tensor factorization (MV-NTF) has attracted wide attention
Ping Yang, Ting-Zhu Huang, Jie Huang, Jin-Ju Wang   +3 more
doaj   +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