Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Liying Xu, Huifang Li, Huanfeng Shen, Mingyang Lei, Tao Jiang

TL;DR

A physical law embedded generative cloud synthesis (PGCS) method is proposed to generate diverse,ealistic 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.

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.

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

TL;DR

A physical law embedded generative cloud synthesis (PGCS) method is proposed to generate diverse,ealistic 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.

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.

Paper Structure

This paper contains 17 sections, 4 equations, 16 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Scattering law for clouds
  4. StyleGAN
  5. Methodology
  6. Spatial synthesis
  7. Spectral synthesis
  8. Paired data synthesis
  9. Results
  10. Cloud synthesis
  11. Verification of synthetic cloud
  12. Cloud synthesis comparison
  13. Application in cloud correction
  14. Discussion
  15. Comparison of synthetic dataset and multi-temporal dataset
  16. ...and 2 more sections

Figures (16)

  • Figure 1: Framework of the proposed PGCS.
  • Figure 2: Patterns of cirrus band images. (a) Pure clouds. (b) With surface information. (c) With stripe noise.
  • Figure 3: Schematic representation of LSGF.
  • Figure 4: Scatter plots of the statistical measures. (a) Mode. (b) Median. (c) Mean.
  • Figure 5: Various synthetic results generated by PGCS. (a) Cloud images with different shapes. (b)-(d) Cloudy images with different cloud thicknesses.
  • ...and 11 more figures