  The adverse haze weather condition has brought considerable difficulties in
vision-based environmental applications. While, until now, most of the existing
environmental monitoring studies are under ordinary conditions, and the studies
of complex haze weather conditions have been ignored. Thence, this paper
proposes a feature-supervised learning network based on generative adversarial
networks (GAN) for environmental monitoring during hazy days. Its main idea is
to train the model under the supervision of feature maps from the ground truth.
Four key technical contributions are made in the paper. First, pairs of hazy
and clean images are used as inputs to supervise the encoding process and
obtain high-quality feature maps. Second, the basic GAN formulation is modified
by introducing perception loss, style loss, and feature regularization loss to
generate better results. Third, multi-scale images are applied as the input to
enhance the performance of discriminator. Finally, a hazy remote sensing
dataset is created for testing our dehazing method and environmental detection.
Extensive experimental results show that the proposed method has achieved
better performance than current state-of-the-art methods on both synthetic
datasets and real-world remote sensing images.
