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Found 14 papers, 4 papers with code
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images.
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images
For Sentinel-2, spectral information fusion allows for enhancing the 20 m and 60 m bands to the 10 m resolution.
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images.
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution
Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs.
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data.
Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series
Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment.
Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation
Hyperspectral imaging provides detailed information about the scanned objects, as it captures their spectral characteristics within a large number of wavelength bands.
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumor.
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images
Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis.
On training deep networks for satellite image super-resolution
The capabilities of super-resolution reconstruction (SRR)---techniques for enhancing image spatial resolution---have been recently improved significantly by the use of deep convolutional neural networks.
Hyperspectral Data Augmentation
Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks.
Deep Learning for Multiple-Image Super-Resolution
Recently, we have witnessed substantial improvement in single-image SRR attributed to the use of deep neural networks for learning the relation between low and high resolution.
Validating Hyperspectral Image Segmentation
Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being rapidly developed.
Real-Time Hand Shape Classification
Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time.
