  Low light conditions in aerial images adversely affect the performance of
several vision based applications. There is a need for methods that can
efficiently remove the low light attributes and assist in the performance of
key vision tasks. In this work, we propose a new method that is capable of
enhancing the low light image in a self-supervised fashion, and sequentially
apply detection and segmentation tasks in an end-to-end manner. The proposed
method occupies a very small overhead in terms of memory and computational
power over the original algorithm and delivers superior results. Additionally,
we propose the generation of a new low light aerial dataset using GANs, which
can be used to evaluate vision based networks for similar adverse conditions.
