Enhanced multi-temporal cloud detection algorithm for optical remote sensing images

被引：0

作者：

Chen X. ^{[1
,2
]}

Zhang X. ^{[2
,3
]}

Liu L. ^{[3
]}

Wang X. ^{[1
]}

机构：

[1] Key Laboratory of Land Use, Ministry of Natural Resources, China Land Surveying and Planning Institute, Beijing

[2] University of Chinese Academy of Sciences, Beijing

[3] Institute of Remote Sensing Science and Digital Earth, CAS, Beijing

来源：

Yaogan Xuebao/Journal of Remote Sensing | 2019年 / 23卷 / 02期

关键词：

Cloud detection; Enhanced Cloud Index; Landsat-8; Multi-spectral; Multi-temporal;

D O I：

10.11834/jrs.20198017

中图分类号：

学科分类号：

摘要：

Remote sensing images have been a crucial data source for land cover mapping and other applications. However, optical remote sensing images are frequently contaminated with clouds. Clouds have caused several limitations in remote sensing applications through optical satellite. Although several approaches have been conducted for cloud detection, they still fail to distinguish bright surfaces, snow, and clouds, especially for seasonally snow-covered images. Therefore, we aim to develop a fast and universal cloud detection method, which can accurately detect clouds in complex areas. Considering that many sensors do not have a thermal infrared band, we only use the visible, near infrared, and short-wave infrared bands to detect clouds. The proposed method is expected to be used for a variety of satellite data. In this study, a multi-temporal cloud detection method was proposed for optical images. Given that snow and clouds have a big difference in short-wave infrared bands, we first developed an Enhanced Cloud Index (ECI) based on the spectral properties of the bands to distinguish them. Then, we proposed an Enhanced Multi-Temporal Cloud Detection (EMTCD) algorithm based on the ECI index and multi-temporal images to extract cloudy pixels. Finally, we tested and compared the algorithm with three classical cloud detection algorithms, namely, Function of mask (Fmask), Cloud Cover Assessment (CCA), and Multi-Temporal Cloud Detection (MTCD) algorithms, to verify the accuracy of the proposed algorithm. Landsat-8 images were used as the data source in this study. Given that many operational cloud detection methods had failed in complex areas, we selected four Landsat-8 OLI scenes in two test areas with typical seasonal snow cover and complicated land covers as our test data. The images were all obtained from 2015. The test areas were the northeast and southwest of China. Test results indicated that the ECI index can effectively distinguish snow and clouds. The ECI index of snow was higher than that of clouds. The EMTCD method performed well in cloud detection, which had the best cloud detection result with an overall accuracy of 93.2% compared with that of 81.85%, 66.14%, and 86.3% for the classic Fmask, MTCD, and CCA cloud detection methods, respectively. The ECI index is effective in distinguishing clouds and snow. The EMTCD algorithm can provide a good performance in cloud detection without using the thermal infrared band, even for seasonally snow-covered regions with complicated high brightness ground surface, which is always challenging for traditional cloud detection algorithms. However, the method is developed based on multiple images. Compared with single temporal methods, the proposed method still has some limitations. © 2019, Science Press. All right reserved.

引用

页码：280 / 290

页数：10

共 32 条

[1]

Ackerman S.A., Strabala K.I., Menzel W.P., Frey R.A., Moeller C.C., Gumley L.E., Discriminating clear sky from clouds with MODIS, Journal of Geophysical Research: Atmospheres, 103, D24, pp. 32141-32157, (1998)

[2]

Braaten J.D., Cohen W.B., Yang Z.Q., Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems, Remote Sensing of Environment, 169, pp. 128-138, (2015)

[3]

Candra D.S., Phinn S., Scarth P., Cloud and cloud shadow masking using multi-temporal cloud masking algorithm in tropical environmental, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41-B2, pp. 95-100, (2016)

[4]

Chander G., Markham B.L., Helder D.L., Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sensing of Environment, 113, 5, pp. 893-903, (2009)

[5]

Chen S.L., Chen X.H., Chen J., Jia P.F., Cao X., Liu C.Y., An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery, IEEE Transactions on Geoscience and Remote Sensing, 54, 5, pp. 2682-2694, (2016)

[6]

Goodwin N.R., Collett L.J., Denham R.J., Flood N., Tindall D., Cloud and cloud shadow screening across Queensland, Australia: an automated method for Landsat TM/ETM+time series, Remote Sensing of Environment, 134, pp. 50-65, (2013)

[7]

Hagolle O., Huc M., Pascual D.V., Dedieu G., A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images, RemoteSensing of Environment, 114, 8, pp. 1747-1755, (2010)

[8]

Hagolle O., Huc M., Pascual D.V., Dedieu G., A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of formosat-2, landsat, VENμS and sentinel-2 images, Remote Sensing, 7, 3, pp. 2668-2691, (2015)

[9]

Hall D.K., Riggs G.A., Salomonson V.V., Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data, Remote Sensing of Environment, 54, 2, pp. 127-140, (1995)

[10]

Hu Y., Liu L.Y., Liu L.L., Peng D.L., Jiao Q.J., Zhang H., A landsat-5 atmospheric correction based on MODIS atmosphere products and 6S model, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 5, pp. 1609-1615, (2014)

← 1 2 3 4 →