PGCS: Physical Law embedded Generative Cloud Synthesis in Remote Sensing Images

TL;DR

The paper introduces PGCS, a novel method embedding physical laws into generative models to synthesize realistic cloud images, improving data quality for remote sensing applications and outperforming existing methods.

Contribution

This work presents a two-phase physical law embedded generative model for realistic cloud synthesis, enhancing data augmentation and cloud correction in remote sensing.

Findings

PGCS achieves high accuracy in spatial and spectral cloud synthesis.

PGCS outperforms three existing cloud synthesis methods.

Enhanced cloud correction methods derived from PGCS show superior performance.

Abstract

Data quantity and quality are both critical for information extraction and analyzation in remote sensing. However, the current remote sensing datasets often fail to meet these two requirements, for which cloud is a primary factor degrading the data quantity and quality. This limitation affects the precision of results in remote sensing application, particularly those derived from data-driven techniques. In this paper, a physical law embedded generative cloud synthesis method (PGCS) is proposed to generate diverse realistic cloud images to enhance real data and promote the development of algorithms for subsequent tasks, such as cloud correction, cloud detection, and data augmentation for classification, recognition, and segmentation. The PGCS method involves two key phases: spatial synthesis and spectral synthesis. In the spatial synthesis phase, a style-based generative adversarial network is utilized to simulate the spatial characteristics, generating an infinite number of single-channel clouds. In the spectral synthesis phase, the atmospheric scattering law is embedded through a local statistics and global fitting method, converting the single-channel clouds into multi-spectral clouds. The experimental results demonstrate that PGCS achieves a high accuracy in both phases and performs better than three other existing cloud synthesis methods. Two cloud correction methods are developed from PGCS and exhibits a superior performance compared to state-of-the-art methods in the cloud correction task. Furthermore, the application of PGCS with data from various sensors was investigated and successfully extended. Code will be provided at https://github.com/Liying-Xu/PGCS.