Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothing

• URL: http://arxiv.org/abs/2212.10081v1
• Date: Tue, 20 Dec 2022 08:46:42 GMT
• Title: Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothing
• Authors: Xiaohua Ma, Xiangru Li, Ali Luo, Jinqu Zhang, Hui Li
• Abstract summary: This paper proposes a novel learning method, Hierarchical Imbalanced data learning with weighted sampling and Label smoothing" (HIWL) The overall classification accuracy is 96.32%, and some superiorities of the HIWL are shown based on recall, precision, and F1-Score.
• Score: 2.7681581852623545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image recognition a hot topic. Available automatic galaxy image recognition researches are plagued by the large differences in similarity between categories, the imbalance of data between different classes, and the discrepancy between the discrete representation of Galaxy classes and the essentially gradual changes from one morphological class to the adjacent class (DDRGC). These limitations have motivated several astronomers and machine learning experts to design projects with improved galaxy image recognition capabilities. Therefore, this paper proposes a novel learning method, ``Hierarchical Imbalanced data learning with Weighted sampling and Label smoothing" (HIWL). The HIWL consists of three key techniques respectively dealing with the above-mentioned three problems: (1) Designed a hierarchical galaxy classification model based on an efficient backbone network; (2) Utilized a weighted sampling scheme to deal with the imbalance problem; (3) Adopted a label smoothing technique to alleviate the DDRGC problem. We applied this method to galaxy photometric images from the Galaxy Zoo-The Galaxy Challenge, exploring the recognition of completely round smooth, in between smooth, cigar-shaped, edge-on and spiral. The overall classification accuracy is 96.32\%, and some superiorities of the HIWL are shown based on recall, precision, and F1-Score in comparing with some related works. In addition, we also explored the visualization of the galaxy image features and model attention to understand the foundations of the proposed scheme.

Related papers

• Ground-based image deconvolution with Swin Transformer UNet [2.41675832913699]
  We introduce a two-step deconvolution framework using a Swin Transformer architecture. Our study reveals that the deep learning-based solution introduces a bias, constraining the scope of scientific analysis. We propose a novel third step relying on the active coefficients in the sparsity wavelet framework.
  arXiv  Detail & Related papers  (2024-05-13T15:30:41Z)
• OsmLocator: locating overlapping scatter marks with a non-training generative perspective [48.50108853199417]
  Locating overlapping marks faces many difficulties such as no texture, less contextual information, hallow shape and tiny size. Here, we formulate it as a optimization problem on clustering-based re-visualization from a non-training generative perspective. We especially built a dataset named 2023 containing hundreds of scatter images with different markers and various levels of overlapping severity, and tested the proposed method and compared it to existing methods.
  arXiv  Detail & Related papers  (2023-12-18T12:39:48Z)
• Symmetrical Bidirectional Knowledge Alignment for Zero-Shot Sketch-Based Image Retrieval [69.46139774646308]
  This paper studies the problem of zero-shot sketch-based image retrieval (ZS-SBIR) It aims to use sketches from unseen categories as queries to match the images of the same category. We propose a novel Symmetrical Bidirectional Knowledge Alignment for zero-shot sketch-based image retrieval (SBKA)
  arXiv  Detail & Related papers  (2023-12-16T04:50:34Z)
• Spiral-Elliptical automated galaxy morphology classification from telescope images [0.40792653193642503]
  We develop two novel galaxy morphology statistics, descent average and descent variance, which can be efficiently extracted from telescope galaxy images. We utilize the galaxy image data from the Sloan Digital Sky Survey to demonstrate the effective performance of our proposed image statistics.
  arXiv  Detail & Related papers  (2023-10-10T22:36:52Z)
• Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection [57.85347204640585]
  We develop a Universal Domain Adaptation method DeepAstroUDA. It can be applied to datasets with different types of class overlap. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets.
  arXiv  Detail & Related papers  (2022-11-01T18:07:21Z)
• Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques [1.3764085113103222]
  Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Here, we train a machine learning model to separate these objects from the foreground stars and background galaxies using the multi-wavelength imaging data of the Fornax galaxy cluster in 6 filters. We are able to identify UCDs/GCs with a precision and a recall of >93 percent and provide relevances that reflect the importance of each feature dimension %(colors and angular sizes)
  arXiv  Detail & Related papers  (2022-01-05T13:37:55Z)
• Morphological Classification of Galaxies in S-PLUS using an Ensemble of Convolutional Networks [0.0]
  We combine accurate visual classifications of the Galaxy Zoo project with emph Deep Learning methods. A neural network model was created through an Ensemble of four other convolutional models, allowing a greater accuracy in the classification than what would be obtained with any one individual.
  arXiv  Detail & Related papers  (2021-07-05T21:51:19Z)
• Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques [0.0]
  This work uses archival data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) to classify radio galaxies into four classes. The model presented in this work is based on Convolutional Neural Networks (CNNs) Our model classified selected classes of radio galaxy sources on an independent testing subset with an average of 96% for precision, recall, and F1 score.
  arXiv  Detail & Related papers  (2021-07-01T11:53:18Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)
• Self-supervised Geometric Perception [96.89966337518854]
  Self-supervised geometric perception is a framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels. We show that SGP achieves state-of-the-art performance that is on-par or superior to the supervised oracles trained using ground-truth labels.
  arXiv  Detail & Related papers  (2021-03-04T15:34:43Z)
• Interpreting Galaxy Deblender GAN from the Discriminator's Perspective [50.12901802952574]
  This research focuses on behaviors of one of the network's major components, the Discriminator, which plays a vital role but is often overlooked. We demonstrate that our method clearly reveals attention areas of the Discriminator when differentiating generated galaxy images from ground truth images.
  arXiv  Detail & Related papers  (2020-01-17T04:05:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.