Celestial Image Classification Using Attention And Boosting Mechanism
subject: Astronomical Image Classification; Convolutional Neural Networks (CNNs); Data Augmentation; Deep Learning; Dimensionality Reduction; Ensemble Learning; Hybrid Models; Transformers; XGBoost
description: Astronomical image classification is vital in the comprehension of celestial objects, but deep learning models are severely challenged by the lack of labeled datasets. The novelty of the study is two-fold - the development of the dataset and a hybrid learning method that combines both transformer-based feature extraction and gradient-boosted decision trees to improve classification performance for celestial image classification. This study is a comparison of CNNs, transformers, and hybrid models in nebulae, galaxy, and star cluster classification using the dataset collected from the Hubble Space Telescope image archive. Through progressive data augmentation, the dataset was augmented from 603 images to 4,500 high-diversity training samples to enhance model generalization. This research explores various architectures, including ResNet-50, DenseNet-121, EfficientNetV2-S, DeiT (Data-Efficient Image Transformer), and hybrid models like DeiT-RF (Data-Efficient Image Transformer - Random Forest) and DeiT-XGBoost (DXg). DXg brings a novel fusion mechanism in which DeiT learns high-level spatial representations, adaptive dimensionality reduction fine-tunes feature selection, and XGBoost best classifies celestial objects. Such a unique combination of transformers and gradient boosting improves interpretability without sacrificing state-of-the-art performance. 2025 IEEE.
creator: Varghese, Nisha; Justin, Jesvin K; Kezya Steffyn, S.
source: 2025 IEEE Space, Aerospace and Defence Conference, SPACE 2025;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833151552-2;
language: English
type: Conference paper
coverage: Varghese N., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Justin J.K., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Kezya Steffyn S., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India