5 research outputs found
Classification of Radio Galaxies with trainable COSFIRE filters
Radio galaxies exhibit a rich diversity of characteristics and emit radio
emissions through a variety of radiation mechanisms, making their
classification into distinct types based on morphology a complex challenge. To
address this challenge effectively, we introduce an innovative approach for
radio galaxy classification using COSFIRE filters. These filters possess the
ability to adapt to both the shape and orientation of prototype patterns within
images. The COSFIRE approach is explainable, learning-free, rotation-tolerant,
efficient, and does not require a huge training set. To assess the efficacy of
our method, we conducted experiments on a benchmark radio galaxy data set
comprising of 1180 training samples and 404 test samples. Notably, our approach
achieved an average accuracy rate of 93.36\%. This achievement outperforms
contemporary deep learning models, and it is the best result ever achieved on
this data set. Additionally, COSFIRE filters offer better computational
performance, 20 fewer operations than the DenseNet-based
competing method (when comparing at the same accuracy). Our findings underscore
the effectiveness of the COSFIRE filter-based approach in addressing the
complexities associated with radio galaxy classification. This research
contributes to advancing the field by offering a robust solution that
transcends the orientation challenges intrinsic to radio galaxy observations.
Our method is versatile in that it is applicable to various image
classification approaches.Comment: 11 pages, 7 figures, submitted for review at MNRAS journa
Deep supervised hashing for fast retrieval of radio image cubes
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
retrieval tasks in the fields of computer vision and multimedia. However, there
are limited applications of these methodologies in the field of astronomy. In
this work, we utilize deep hashing to rapidly search for similar images in a
large database. The experiment uses a balanced dataset of 2708 samples
consisting of four classes: Compact, FRI, FRII, and Bent. The performance of
the method was evaluated using the mean average precision (mAP) metric where a
precision of 88.5\% was achieved. The experimental results demonstrate the
capability to search and retrieve similar radio images efficiently and at
scale. The retrieval is based on the Hamming distance between the binary hash
of the query image and those of the reference images in the database.Comment: 4 pages, 4 figure
Advances on the morphological classification of radio galaxiesreview: A review
Modern radio telescopes will generate, on a daily basis, data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of machine learning to complement human-aided and traditional statistical techniques. Recently, there has been a surge in scientific publications focusing on the use of machine/deep learning in radio astronomy, addressing challenges such as source extraction, morphological classification, and anomaly detection. This study provides a comprehensive and concise overview of the use of machine learning techniques for the morphological classification of radio galaxies. It summarizes the recent literature on this topic, highlighting the main challenges, achievements, state-of-the-art methods, and the future research directions in the field. The application of machine learning in radio astronomy has led to a new paradigm shift and a revolution in the automation of complex data processes. However, the optimal exploitation of machine/deep learning in radio astronomy, calls for continued collaborative efforts in the creation of high-resolution annotated data sets. This is especially true in the case of modern telescopes like MeerKAT and the LOw-Frequency ARray (LOFAR). Additionally, it is important to consider the potential benefits of utilizing multi-channel data cubes and algorithms that can leverage massive datasets without relying solely on annotated datasets for radio galaxy classification.<br/
Advances on the classification of radio image cubes
Modern radio telescopes will daily generate data sets on the scale of
exabytes for systems like the Square Kilometre Array (SKA). Massive data sets
are a source of unknown and rare astrophysical phenomena that lead to
discoveries. Nonetheless, this is only plausible with the exploitation of
intensive machine intelligence to complement human-aided and traditional
statistical techniques. Recently, there has been a surge in scientific
publications focusing on the use of artificial intelligence in radio astronomy,
addressing challenges such as source extraction, morphological classification,
and anomaly detection. This study presents a succinct, but comprehensive review
of the application of machine intelligence techniques on radio images with
emphasis on the morphological classification of radio galaxies. It aims to
present a detailed synthesis of the relevant papers summarizing the literature
based on data complexity, data pre-processing, and methodological novelty in
radio astronomy. The rapid advancement and application of computer intelligence
in radio astronomy has resulted in a revolution and a new paradigm shift in the
automation of daunting data processes. However, the optimal exploitation of
artificial intelligence in radio astronomy, calls for continued collaborative
efforts in the creation of annotated data sets. Additionally, in order to
quickly locate radio galaxies with similar or dissimilar physical
characteristics, it is necessary to index the identified radio sources.
Nonetheless, this issue has not been adequately addressed in the literature,
making it an open area for further study.Comment: 21 page review paper submitted to New astronomy reviews journal for
revie
Deep supervised hashing for fast retrieval of radio image cubes
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 retrieval tasks in the fields of computer vision and multimedia. However, there are limited applications of these methodologies in the field of astronomy. In this work, we utilize deep hashing to rapidly search for similar images in a large database. The experiment uses a balanced dataset of 2708 samples consisting of four classes: Compact, FRI, FRII, and Bent. The performance of the method was evaluated using the mean average precision (mAP) metric where a precision of 88.5% was achieved. The experimental results demonstrate the capability to search and retrieve similar radio images efficiently and at scale. The retrieval is based on the Hamming distance between the binary hash of the query image and those of the reference images in the database.</p