Automated Cloud and Cloud-Shadow Masking for Landsat 8 Using Multitemporal Images in a Variety of Environments

Autor: Stuart R. Phinn, Danang Surya Candra, Peter Scarth
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Remote Sensing; Volume 11; Issue 17; Pages: 2060
Remote Sensing, Vol 11, Iss 17, p 2060 (2019)
ISSN: 2072-4292
DOI: 10.3390/rs11172060
Popis: Landsat 8 images have been widely used for many applications, but cloud and cloud-shadow cover issues remain. In this study, multitemporal cloud masking (MCM), designed to detect cloud and cloud-shadow for Landsat 8 in tropical environments, was improved for application in sub-tropical environments, with the greatest improvement in cloud masking. We added a haze optimized transformation (HOT) test and thermal band in the previous MCM algorithm to improve the algorithm in the detection of haze, thin-cirrus cloud, and thick cloud. We also improved the previous MCM in the detection of cloud-shadow by adding a blue band. In the visual assessment, the algorithm can detect a thick cloud, haze, thin-cirrus cloud, and cloud-shadow accurately. In the statistical assessment, the average user’s accuracy and producer’s accuracy of cloud masking results across the different land cover in the selected area was 98.03% and 98.98%, respectively. On the other hand, the average user’s accuracy and producer’s accuracy of cloud-shadow masking results was 97.97% and 96.66%, respectively. Compared to the Landsat 8 cloud cover assessment (L8 CCA) algorithm, MCM has better accuracies, especially in cloud-shadow masking. Our preliminary tests showed that the new MCM algorithm can detect cloud and cloud-shadow for Landsat 8 in a variety of environments.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje