Related papers: Solar Filament Recognition Based on Deep Learning
Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and…
With the advent of deep learning for computer vision tasks, the need for accurately labeled data in large volumes is vital for any application. The increasingly available large amounts of solar image data generated by the Solar Dynamic…
One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based…
Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This…
The shear number of sources that will be detected by next-generation radio surveys will be astronomical, which will result in serendipitous discoveries. Data-dependent deep hashing algorithms have been shown to be efficient at image…
The morphology of circular polarisation profiles from solar spectropolarimetric observations encode information about the magnetic field strength, inclination, and line-of-sight velocity gradients. Previous studies used manual methods or…
We developed an automated method for sunspot detection using digital white-light solar images to achieve a performance similar to that of visual drawing observations in sunspot counting. To identify down to small, isolated spots correctly,…
Solar filaments show the position of large scale polarity inversion lines and are used for the reconstruction of large-scale solar magnetic field structure on the basis of H{\alpha} synoptic charts for the periods when magnetographic…
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale…
Solar flares are energetic events in the solar atmosphere that are often linked with solar radio bursts (SRBs). SRBs are observed at metric to decametric wavelengths and are classified into five spectral classes (Type I--V) based on their…
The century long (1914-2007) $H_{\alpha}$ (656.28 nm) spectroheliograms from Kodaikanal Solar Observatory (KSO) have been recently digitised. Using these newly calibrated, processed images we study the evolution of dark elongated on disk…
Solar flares are intense eruptive events caused by the rapid release of magnetic energy, often impacting Earth's space environment through electromagnetic radiation and high-energy particles. Accurate flare prediction is critical for space…
Satellite remote imaging enables the detailed study of land use patterns on a global scale. We investigate the possibility to improve the information content of traditional land use classification by identifying the nature of industrial…
The automated detection of solar features is a technique which is relatively underused but if we are to keep up with the flow of data from spacecraft such as the recently launched Solar Dynamics Observatory, then such techniques will be…
The deep neural network is a research hotspot for histopathological image analysis, which can improve the efficiency and accuracy of diagnosis for pathologists or be used for disease screening. The whole slide pathological image can reach…
In this paper, we proposed using a hybrid method that utilises deep convolutional and recurrent neural networks for accurate delineation of skin lesion of images supplied with ISBI 2017 lesion segmentation challenge. The proposed method was…
In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early identification of failures has become crucial to maintain productivity and prolong components' life. Of all defects, cell-level anomalies can lead to serious…
We present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies…
High-resolution (HR) solar imaging is crucial for capturing fine-scale dynamic features such as filaments and fibrils. However, the spatial resolution of the full-disk H$\alpha$ images is limited and insufficient to resolve these…
We consider the problem of automatically detecting small-scale solar photovoltaic arrays for behind-the-meter energy resource assessment in high resolution aerial imagery. Such algorithms offer a faster and more cost-effective solution to…