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

TL;DR

This paper introduces SAGE-NDVI, a new evaluation metric for remote sensing image dehazing that correlates well with human perception by using ground-based vegetation indices.

Contribution

The paper proposes a novel objective metric for RS image dehazing evaluation that leverages ground-based NDVI data, improving assessment accuracy over traditional metrics.

Findings

The new metric aligns closely with human visual perception.

It effectively evaluates different dehazing models.

The metric utilizes ground-based phenology data for assessment.

Abstract

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.