Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V Images for Cloud Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Dan Lopez-Puigdollers¹  Luis Gomez-Chova¹  Gonzalo Mateo-Garcia¹  Valero Laparra¹ 

[1] Image Processing Laboratory (IPL), University of Valencia, Valencia, Spain;

关键词: Generative adversarial networks;    convolutional neural networks;    domain adaptation;    Landsat-8;    Proba-V;    cloud detection;   

DOI  :  10.1109/JSTARS.2020.3031741

来源: DOAJ

【 摘 要 】

The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor independently. Differences in retrieved radiances lead to significant drops in accuracy, which hampers knowledge and information sharing across sensors. This is particularly harmful for machine learning algorithms, since gathering new ground-truth data to train models for each sensor is costly and requires experienced manpower. In this work, we propose a domain adaptation transformation to reduce the statistical differences between images of two satellite sensors in order to boost the performance of transfer learning models. The proposed methodology is based on the cycle consistent generative adversarial domain adaptation framework that trains the transformation model in an unpaired manner. In particular, Landsat-8 and Proba-V satellites, which present different but compatible spatio-spectral characteristics, are used to illustrate the method. The obtained transformation significantly reduces differences between the image datasets while preserving the spatial and spectral information of adapted images, which is, hence, useful for any general purpose cross-sensor application. In addition, the training of the proposed adversarial domain adaptation model can be modified to improve the performance in a specific remote sensing application, such as cloud detection, by including a dedicated term in the cost function. Results show that, when the proposed transformation is applied, cloud detection models trained in Landsat-8 data increase cloud detection accuracy in Proba-V.

【 授权许可】

Unknown