SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing
  Image Dehazing Using Satellite-to-Ground NDVI Knowledge
Zepeng Liu Zhicheng Yang Mingye Zhu Andy Wong Yibing Wei Mei Han Jun Yu Jui-Hsin Lai
Image dehazing is a meaningful low-level computer vision task and can be
applied to a variety of contexts. In our industrial deployment scenario based
on remote sensing (RS) images, the quality of image dehazing directly affects
the grade of our crop identification and growth monitoring products. However,
the widely used peak signal-to-noise ratio (PSNR) and structural similarity
index (SSIM) provide ambiguous visual interpretation. In this paper, we design
a new objective metric for RS image dehazing evaluation. Our proposed metric
leverages a ground-based phenology observation resource to calculate the
vegetation index error between RS and ground images at a hazy date. Extensive
experiments validate that our metric appropriately evaluates different dehazing
models and is in line with human visual perception.