Detection, instance segmentation, and classification for astronomical surveys with deep learning (deepdisc): detectron2 implementation and demonstration with …
The next generation of wide-field deep astronomical surveys will deliver unprecedented
amounts of images through the 2020s and beyond. As both the sensitivity and depth of …
amounts of images through the 2020s and beyond. As both the sensitivity and depth of …
Deblending and classifying astronomical sources with Mask R-CNN deep learning
We apply a new deep learning technique to detect, classify, and deblend sources in
multiband astronomical images. We train and evaluate the performance of an artificial neural …
multiband astronomical images. We train and evaluate the performance of an artificial neural …
Deep learning applications based on SDSS photometric data: detection and classification of sources
Z He, B Qiu, AL Luo, J Shi, X Kong… - Monthly Notices of the …, 2021 - academic.oup.com
Most astronomical source classification algorithms based on photometric data struggle to
classify sources as quasars, stars, and galaxies reliably. To achieve this goal and build a …
classify sources as quasars, stars, and galaxies reliably. To achieve this goal and build a …
Satellite imagery multiscale rapid detection with windowed networks
A Van Etten - 2019 IEEE winter conference on applications of …, 2019 - ieeexplore.ieee.org
Detecting small objects over large areas remains a significant challenge in satellite imagery
analytics. Among the challenges is the sheer number of pixels and geographical extent per …
analytics. Among the challenges is the sheer number of pixels and geographical extent per …
Learning efficient and accurate detectors with dynamic knowledge distillation in remote sensing imagery
Deep convolutional neural networks (CNNs) have brought a tremendous increase in
detection accuracy, but too cumbersome model makes them hard to deploy on low …
detection accuracy, but too cumbersome model makes them hard to deploy on low …
Yolo-dcti: small object detection in remote sensing base on contextual transformer enhancement
Object detection for remote sensing is a fundamental task in image processing of remote
sensing; as one of the core components, small or tiny object detection plays an important …
sensing; as one of the core components, small or tiny object detection plays an important …
Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data
R Hausen, BE Robertson - The Astrophysical Journal …, 2020 - iopscience.iop.org
We present Morpheus, a new model for generating pixel-level morphological classifications
of astronomical sources. Morpheus leverages advances in deep learning to perform source …
of astronomical sources. Morpheus leverages advances in deep learning to perform source …
Detection and classification of astronomical targets with deep neural networks in wide-field small aperture telescopes
Wide-field small aperture telescopes are widely used for optical transient observations.
Detection and classification of astronomical targets in observed images are the most …
Detection and classification of astronomical targets in observed images are the most …
You only look twice: Rapid multi-scale object detection in satellite imagery
A Van Etten - arXiv preprint arXiv:1805.09512, 2018 - arxiv.org
Detection of small objects in large swaths of imagery is one of the primary problems in
satellite imagery analytics. While object detection in ground-based imagery has benefited …
satellite imagery analytics. While object detection in ground-based imagery has benefited …
Convolutional neural networks for transient candidate vetting in large-scale surveys
Current synoptic sky surveys monitor large areas of the sky to find variable and transient
astronomical sources. As the number of detections per night at a single telescope easily …
astronomical sources. As the number of detections per night at a single telescope easily …