Optical remote sensing cloud detection based on random forest only using the visible light and near-infrared image bands

Cloud detection is of great significance for optical remote sensing images. Most existing cloud detection approaches often rely on many thresholds among multiple bands with a wide spectrum range, and normally just can be applied to specific satellite data. Cloud detection is difficult in some sensor data with limited number of available spectral bands. To tackle this challenge, we propose a cloud detection method based on random forest (RFCD) only with the most common RGB and NIR bands. The RFCD normalizes different sensor data by calculating the top-of-atmosphere (TOA) reflectance and combines stable spectral and texture features extracted from the most common bands, which improves accuracy, reduces reliance on multi-band information, and improves the extendibility of the method. Moreover, the RFCD effectively avoids the complicated threshold setting and reduces subjective factors. Few parameters and strong generalization ability of RF further improve the extendibility of RFCD and provide the application possibility in a variety of data. Experimental results show the total validity obtained by 107 Landsat-8 images is 93.46%. The accuracy of the RFCD is higher than the Function of mask method and similar to the SegNet deep learning method, while the RFCD need fewer training samples and hyperparameters which makes it easier to be used. The extended experimental results show RFCD also gets good detection results in Sentinel-2 and GaoFen-1 images. The new RFCD is an accurate cloud detection method with strong extendibility and stability, also avoiding the complicated threshold setting.

Automated summary

This paper proposes a cloud detection method based on random forest, using only the most common RGB and NIR bands. The method normalizes sensor data, combines spectral and texture features, and improves accuracy and extendibility, while avoiding complex threshold settings.