Consistent Photometric Enhancement Network for Remote Sensing Change Detection

Abstract

Change detection performance in remote sensing is highly sensitive to illumination variations between bi-temporal images. In real-world scenarios, low-light conditions and inconsistent brightness often lead to degraded feature representations and unreliable detection results. To address this issue, this paper proposes a Consistent Photometric Enhancement Network (CPEN) for change detection under complex illumination conditions. Unlike conventional low-light enhancement methods applied independently to each image, CPEN explicitly enforces photometric consistency between bi-temporal images before and during an enhancement procedure. The proposed framework applies a low-light compensation mechanism to reduce photometric discrepancies between image pairs, which is followed by a bi-temporal enhancement module that jointly improves brightness while preserving shared structural information. Using the photometrically consistent and enhanced images, a change detection module is employed to extract reliable features and generate accurate change maps. Experimental results show that CPEN achieves F1/IoU scores of 90.39/82.67, 80.71/70.58, 78.34/64.26, and 86.26/79.02 on LEVIR-CD, PRCV-CD, SYSU-CD, and the real-world NPULL-CD dataset, respectively, demonstrating its robustness under both simulated and real illumination variations.