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07/14/2023
Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
Support vector machines (SVMs) are a well-established classifier effecti...
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06/26/2023
Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images
The optimization of Kernel-Target Alignment (TA) has been recently propo...
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06/16/2023
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
Cloud detection is a pivotal satellite image pre-processing step that ca...
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02/16/2023
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
Support vector machines (SVMs) are a well-established classifier effecti...
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01/26/2023
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images
Multispectral Sentinel-2 images are a valuable source of Earth observati...
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10/24/2022
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
Cloud detection is a pivotal satellite image pre-processing step that ca...
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10/06/2022
MuS2: A Benchmark for Sentinel-2 Multi-Image Super-Resolution
Insufficient spatial resolution of satellite imagery, including Sentinel...
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09/03/2022
Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients
Tumor burden assessment by magnetic resonance imaging (MRI) is central t...
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08/03/2022
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
Hyperspectral unmixing remains one of the most challenging tasks in the ...
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08/03/2022
Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series
Maintaining farm sustainability through optimizing the agricultural mana...
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07/27/2019
Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation
Hyperspectral imaging provides detailed information about the scanned ob...
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07/20/2019
Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders
Hyperspectral image analysis has become an important topic widely resear...
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07/18/2019
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an ...
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06/23/2019
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images
Deep learning has established the state of the art in multiple fields, i...
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06/16/2019
On training deep networks for satellite image super-resolution
The capabilities of super-resolution reconstruction (SRR)---techniques f...
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03/13/2019
Hyperspectral Data Augmentation
Data augmentation is a popular technique which helps improve generalizat...
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03/01/2019
Deep Learning for Multiple-Image Super-Resolution
Super-resolution reconstruction (SRR) is a process aimed at enhancing sp...
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11/08/2018
Validating Hyperspectral Image Segmentation
Hyperspectral satellite imaging attracts enormous research attention in ...
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11/05/2018
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different d...
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02/27/2014