  We present a two-component Machine Learning (ML) based approach for
classifying astronomical images by data-quality via an examination of sources
detected in the images and image pixel values from representative sources
within those images. The first component, which uses a clustering algorithm,
creates a proper and small fraction of the image pixels to determine the
quality of the observation. The representative images (and associated tables)
are ~800 times smaller than the original images, significantly reducing the
time required to train our algorithm. The useful information in the images is
preserved, permitting them to be classified in different categories, but the
required storage is reduced. The second component, which is a deep neural
network model, classifies the representative images. Using ground-based
telescope imaging data, we demonstrate that the method can be used to separate
'usable' images from those that present some problems for scientific projects
-- such as images that were taken in sub-optimal conditions. This method uses
two different data sets as input to a deep model and provides better
performance than if we only used the images' pixel information. The method may
be used in cases where large and complex data sets should be examined using
deep models. Our automated classification approach achieves 97% agreement when
compared to classification generated via manual image inspection. We compare
our method with traditional results and show that the method improves the
results by about 10%, and also presents more comprehensive outcomes.
