SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing Image Dehazing Using Satellite-to-Ground NDVI Knowledge
This work addresses the need for better evaluation metrics in remote sensing image dehazing to improve crop identification and growth monitoring products, but it is incremental as it focuses on a specific domain metric.
The authors tackled the problem of evaluating remote sensing image dehazing by designing a new objective metric that uses satellite-to-ground NDVI knowledge to calculate vegetation index errors, and validated it through experiments to align with human visual perception.
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.