KDAD: Knowledge Distillation-Based Anomaly Detection for Thermal Infrared Hyperspectral Image
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingAutoencoder (AE) is extensively utilized in hyperspectral anomaly detection (HAD) tasks owing to its robust feature extraction and image reconstruction capabilities. However, AE lacks constraints on anomaly samples during the training process, leading to
Enyu Zhao +4 more
doaj +1 more source
Deep learning classifiers for hyperspectral imaging: A review
, 2019 Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications.
Mercedes Eugenia Paoletti +3 more
semanticscholar +1 more source
Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery [PDF]
, 2008 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 ...
Chang, Chein-I +5 more
core +1 more source
Advancing Fruit Bioimpedance Monitoring With Sustainable, Soft, And Bio‐Based Electrodes Beyond ECG
Advanced Electronic Materials, EarlyView.Electrical impedance spectroscopy enables non‐destructive fruit quality monitoring, but conventional ECG and needle electrodes compromise signal stability, fruit physiology, and sustainability. This perspective highlights the transition toward soft, biocompatible, and biodegradable electrode interfaces based on natural substrates, bio‐derived ...
Sundus Riaz +6 more
wiley +1 more source
Editorial for Special Issue “Hyperspectral Imaging and Applications”
Remote Sensing, 2019 Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging.
Chein-I Chang +3 more
doaj +1 more source
A compressive hyperspectral video imaging system using a single-pixel detector
Nature CommunicationsCapturing fine spatial, spectral, and temporal information of the scene is highly desirable in many applications. However, recording data of such high dimensionality requires significant transmission bandwidth.
Yibo Xu +3 more
semanticscholar +1 more source
Singular spectrum analysis for effective feature extraction in hyperspectral imaging
, 2014 As a very recent technique for time series analysis, Singular Spectrum Analysis (SSA) has been applied in many diverse areas, where an original 1D signal can be decomposed into a sum of components including varying trends, oscillations and noise ...
Zabalza, Jaime +4 more
core +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
MORPHOLOGICAL SEGMENTATION OF HYPERSPECTRAL IMAGES
Image Analysis & Stereology, 2011 The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i.e., with an important number of channels. This approach, based on watershed, is composed of a spectral classification to obtain the markers and a vectorial gradient which gives the spatial information. Several alternative gradients are adapted
Noyel, Guillaume +2 more
openaire +7 more sources
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.
Eches, Olivier +2 more
core +1 more source