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Image dehazing is a meaningful low-level computer vision task and can beapplied to a variety of contexts. In our industrial deployment scenario basedon remote sensing (RS) images, the quality of image dehazing directly affectsthe grade of our crop identification and growth monitoring products. However,the widely used peak signal-to-noise ratio (PSNR) and structural similarityindex (SSIM) provide ambiguous visual interpretation. In this paper, we designa new objective metric for RS image dehazing evaluation. Our proposed metricleverages a ground-based phenology observation resource to calculate thevegetation index error between RS and ground images at a hazy date. Extensiveexperiments validate that our metric appropriately evaluates different dehazingmodels and is in line with human visual perception.

SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing Image Dehazing Using Satellite-to-Ground NDVI Knowledge