scholarly journals Fanaroff–Riley classification of radio galaxies using group-equivariant convolutional neural networks

2021 ◽  
Vol 503 (2) ◽  
pp. 2369-2379
Author(s):  
Anna M M Scaife ◽  
Fiona Porter
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Radio Galaxy ◽  
Epistemic Uncertainty ◽  
Parameter Tuning ◽  
Radio Galaxies ◽  
Input Image ◽  
Feature Maps ◽  
Galaxy Classification

ABSTRACT Weight sharing in convolutional neural networks (CNNs) ensures that their feature maps will be translation-equivariant. However, although conventional convolutions are equivariant to translation, they are not equivariant to other isometries of the input image data, such as rotation and reflection. For the classification of astronomical objects such as radio galaxies, which are expected statistically to be globally orientation invariant, this lack of dihedral equivariance means that a conventional CNN must learn explicitly to classify all rotated versions of a particular type of object individually. In this work we present the first application of group-equivariant convolutional neural networks to radio galaxy classification and explore their potential for reducing intra-class variability by preserving equivariance for the Euclidean group E(2), containing translations, rotations, and reflections. For the radio galaxy classification problem considered here, we find that classification performance is modestly improved by the use of both cyclic and dihedral models without additional hyper-parameter tuning, and that a D16 equivariant model provides the best test performance. We use the Monte Carlo Dropout method as a Bayesian approximation to recover epistemic uncertainty as a function of image orientation and show that E(2)-equivariant models are able to reduce variations in model confidence as a function of rotation.

scholarly journals Attention-gating for improved radio galaxy classification

2020 ◽  
Author(s):  
Micah Bowles ◽  
Anna M M Scaife ◽  
Fiona Porter ◽  
Hongming Tang ◽  
David J Bastien
Keyword(s):  
Neural Networks ◽  
Radio Galaxy ◽  
State Of The Art ◽  
Radio Galaxies ◽  
Radio Sources ◽  
Effective Manner ◽  
Salient Regions ◽  
Galaxy Classification ◽  
Selection Of

Abstract In this work we introduce attention as a state of the art mechanism for classification of radio galaxies using convolutional neural networks. We present an attention-based model that performs on par with previous classifiers while using more than 50% fewer parameters than the next smallest classic CNN application in this field. We demonstrate quantitatively how the selection of normalisation and aggregation methods used in attention-gating can affect the output of individual models, and show that the resulting attention maps can be used to interpret the classification choices made by the model. We observe that the salient regions identified by the our model align well with the regions an expert human classifier would attend to make equivalent classifications. We show that while the selection of normalisation and aggregation may only minimally affect the performance of individual models, it can significantly affect the interpretability of the respective attention maps and by selecting a model which aligns well with how astronomers classify radio sources by eye, a user can employ the model in a more effective manner.

scholarly journals Novel convolutional neural networks for efficient classification of rotated and scaled images

2021 ◽  
Author(s):  
Paweł Tarasiuk ◽  
Piotr S. Szczepaniak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Network Models ◽  
Input Image ◽  
Feature Maps ◽  
Neural Network Models ◽  
Geometric Transformations

AbstractThis paper presents a novel method for improving the invariance of convolutional neural networks (CNNs) to selected geometric transformations in order to obtain more efficient image classifiers. A common strategy employed to achieve this aim is to train the network using data augmentation. Such a method alone, however, increases the complexity of the neural network model, as any change in the rotation or size of the input image results in the activation of different CNN feature maps. This problem can be resolved by the proposed novel convolutional neural network models with geometric transformations embedded into the network architecture. The evaluation of the proposed CNN model is performed on the image classification task with the use of diverse representative data sets. The CNN models with embedded geometric transformations are compared to those without the transformations, using different data augmentation setups. As the compared approaches use the same amount of memory to store the parameters, the improved classification score means that the proposed architecture is more optimal.

scholarly journals Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

2018 ◽  
Vol 10 (10) ◽  
pp. 1636 ◽  
Author(s):  
Diogo Duarte ◽  
Francesco Nex ◽  
Norman Kerle ◽  
George Vosselman
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Binary Classification ◽  
Feature Maps ◽  
Intermediate Layers ◽  
Resolution Level

Remote sensing images have long been preferred to perform building damage assessments. The recently proposed methods to extract damaged regions from remote sensing imagery rely on convolutional neural networks (CNN). The common approach is to train a CNN independently considering each of the different resolution levels (satellite, aerial, and terrestrial) in a binary classification approach. In this regard, an ever-growing amount of multi-resolution imagery are being collected, but the current approaches use one single resolution as their input. The use of up/down-sampled images for training has been reported as beneficial for the image classification accuracy both in the computer vision and remote sensing domains. However, it is still unclear if such multi-resolution information can also be captured from images with different spatial resolutions such as imagery of the satellite and airborne (from both manned and unmanned platforms) resolutions. In this paper, three multi-resolution CNN feature fusion approaches are proposed and tested against two baseline (mono-resolution) methods to perform the image classification of building damages. Overall, the results show better accuracy and localization capabilities when fusing multi-resolution feature maps, specifically when these feature maps are merged and consider feature information from the intermediate layers of each of the resolution level networks. Nonetheless, these multi-resolution feature fusion approaches behaved differently considering each level of resolution. In the satellite and aerial (unmanned) cases, the improvements in the accuracy reached 2% while the accuracy improvements for the airborne (manned) case was marginal. The results were further confirmed by testing the approach for geographical transferability, in which the improvements between the baseline and multi-resolution experiments were overall maintained.

scholarly journals Radio Galaxy Classification: #Tags, Not Boxes

Galaxies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 85
Author(s):  
Lawrence Rudnick
Keyword(s):  
Radio Galaxy ◽  
Radio Galaxies ◽  
Physical Understanding ◽  
Galaxy Classification

After six decades of studying radio galaxies, we are now delightfully overwhelmed by their exponentially expanding numbers and the complexity of their structures. Similarly, the methods we use to classify radio galaxies have exploded, often resulting in conflicting terminology, ambiguous classifications, and historical schemes that may or may not match our current physical understanding. After discussions with more than 100 radio astronomers over the last several years and listening to their ideas and aspirations, I propose that we reconceptualize the classification of radio galaxies. Instead of trying to place them into “boxes”, we should assign them #tags, a system that is easy to understand and apply, that is flexible and evolving, and that can accommodate conflicting ideas with respect to what is relevant and important. Here, I outline the basis of such a #tag system; the rest is up to the community.

scholarly journals Morphological classification of radio galaxies: capsule networks versus convolutional neural networks

2019 ◽  
Vol 487 (2) ◽  
pp. 1729-1744 ◽  
Author(s):  
V Lukic ◽  
M Brüggen ◽  
B Mingo ◽  
J H Croston ◽  
G Kasieczka ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Radio Galaxies ◽  
Morphological Classification

scholarly journals CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS

2018 ◽  
Vol IV-3 ◽  
pp. 251-258 ◽  
Author(s):  
Chun Yang ◽  
Franz Rottensteiner ◽  
Christian Heipke
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Input Data ◽  
Input Image ◽  
Aerial Images ◽  
Positive Contribution ◽  
Land Use Classification

Land cover describes the physical material of the earth’s surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7 % and 77.4 % can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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