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.
ABSTRACT:A cloud masking approach based on multi-temporal satellite images is proposed. The basic idea of this approach is to detect cloud and cloud shadow by using the difference reflectance values between clear pixels and cloud and cloud shadow contaminated pixels. Several bands of satellite image which have big difference values are selected for developing Multi-temporal Cloud Masking (MCM) algorithm. Some experimental analyses are conducted by using Landsat-8 images. Band 3 and band 4 are selected because they can distinguish between cloud and non cloud. Afterwards, band 5 and band 6 are used to distinguish between cloud shadow and clear. The results show that the MCM algorithm can detect cloud and cloud shadow appropriately. Moreover, qualitative and quantitative assessments are conducted using visual inspections and confusion matrix, respectively, to evaluate the reliability of this algorithm. Comparison between this algorithm and QA band are conducted to prove the reliability of the approach. The results show that MCM better than QA band and the accuracy of the results are very high.
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