  The intersection of Astronomy and AI encounters significant challenges
related to issues such as noisy backgrounds, lower resolution (LR), and the
intricate process of filtering and archiving images from advanced telescopes
like the James Webb. Given the dispersion of raw images in feature space, we
have proposed a \textit{two-stage augmentation framework} entitled as
\textbf{FLARE} based on \underline{f}eature \underline{l}earning and
\underline{a}ugmented \underline{r}esolution \underline{e}nhancement. We first
apply lower (LR) to higher resolution (HR) conversion followed by standard
augmentations. Secondly, we integrate a diffusion approach to synthetically
generate samples using class-concatenated prompts. By merging these two stages
using weighted percentiles, we realign the feature space distribution, enabling
a classification model to establish a distinct decision boundary and achieve
superior generalization on various in-domain and out-of-domain tasks. We
conducted experiments on several downstream cosmos datasets and on our
optimally distributed \textbf{SpaceNet} dataset across 8-class fine-grained and
4-class macro classification tasks. FLARE attains the highest performance gain
of 20.78\% for fine-grained tasks compared to similar baselines, while across
different classification models, FLARE shows a consistent increment of an
average of +15\%. This outcome underscores the effectiveness of the FLARE
method in enhancing the precision of image classification, ultimately
bolstering the reliability of astronomical research outcomes. % Our code and
SpaceNet dataset will be released to the public soon. Our code and SpaceNet
dataset is available at
\href{https://github.com/Razaimam45/PlanetX_Dxb}{\textit{https://github.com/Razaimam45/PlanetX\_Dxb}}.
