PGCS: Physical Law embedded Generative Cloud Synthesis in Remote Sensing Images
Liying Xu, Huifang Li, Huanfeng Shen, Mingyang Lei, and Tao Jiang

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…
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Taxonomy
TopicsAdvanced Image Fusion Techniques · Remote Sensing in Agriculture
