Results 71 to 80 of about 153,077 (360)
Enhancing hyperspectral image unmixing with spatial correlations [PDF]
, 2010 This paper describes a new algorithm for hyperspectral image unmixing. Most unmixing algorithms proposed in the literature do not take into account the possible spatial correlations between the pixels.
Jean-Yves Tourneret +5 more
core +1 more source
Advanced Materials, EarlyView.In this work, low bandgap (≈1.55 eV) semitransparent perovskite solar cells (ST‐PSCs) having thin (thickness < ≈100 nm) perovskite layers and transparent conductive oxide‐based rear electrodes are fabricated using vacuum‐deposition methods. Two different ST‐PSCs demonstrated a high light utilization efficiency value of 4.2 (PCE: 9.26% and AVT: 45.3 ...
Abhyuday Paliwal +9 more
wiley +1 more source
MEETNet: Morphology-Edge Enhanced Triple-Cascaded Network for Infrared Small Target Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingInfrared small target detection is focused on accurately identifying tiny targets with low signal-to-noise ratio against complex backgrounds, representing a critical challenge in the field of infrared image processing. Existing approaches frequently fail
Enyu Zhao +4 more
doaj +1 more source
Ultra‐Confinement of Polaritons in Single Atomic Layer Ag Photonic Quantum Dots
Advanced Materials, EarlyView.Strong confinement of surface phonon polaritons of silicon carbide was achieved in photonic quantum dots fabricated from van der Waals heterostructure of epitaxial graphene over single‐atomic layer silver. Scattering‐type scanning near‐field optical microscopy combined with an analytical approach, developed to reveal local propagation constants with ...
Xinyi Li +10 more
wiley +1 more source
Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification
Remote Sensing, 2018 Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks.
Mercedes E. Paoletti +3 more
doaj +1 more source
A Comprehensive Survey of Imbalance Correction Techniques for Hyperspectral Data Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Land-cover classification is an important topic for remotely sensed hyperspectral (HS) data exploitation. In this regard, HS classifiers have to face important challenges, such as the high spectral redundancy, as well as noise, present in the data, and ...
Mercedes E. Paoletti +4 more
doaj +1 more source
Hyperspectral Data Augmentation
CoRR, 2019 Submitted to IEEE Geoscience and Remote Sensing ...
Jakub Nalepa +2 more
openaire +2 more sources
Hyperspectral compressive wavefront sensing
High Power Laser Science and Engineering, 2023 Abstract Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative ...
Sunny Howard +4 more
openaire +4 more sources
Remote Sensing, 2020 Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on ...
A. Vali, S. Comai, M. Matteucci
semanticscholar +1 more source
Deep Learning for Classification of Hyperspectral Data: A Comparative Review [PDF]
IEEE Geoscience and Remote Sensing Magazine, 2019 In recent years, deep-learning techniques revolutionized the way remote sensing data are processed. The classification of hyperspectral data is no exception to the rule, but it has intrinsic specificities that make the application of deep learning less ...
N. Audebert +2 more
semanticscholar +1 more source