TY - GEN
T1 - Cloud-cover assessment
T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
AU - Wang, Shuigen
AU - Deng, Chenwei
AU - Liu, Xun
AU - Li, Zhenzhen
AU - Feng, Fan
AU - Zhao, Baojun
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Cloud contamination is the most common defect leading to quality degradation in remote sensing images. Numerous cloud-cover assessment (CCA) methods have been developed in the literature. The traditional Landsat 7 CCA algorithm attempted to detect clouds by taking advantages of different spectral properties from five spectral bands. However, it suffers the weakness of omitting thin cirrus clouds and the requirement of thermal bands. In this paper, we derived an automated CCA (ACCA) model that measures statistical deviations in spatial domain between cloud and clear images. Moreover, it only conducts on panchromatic band image, which can successfully address the limitation of unavailable thermal bands for satellite missions without thermal infrared sensors on board. A database with 400 clear/cloud images is then built for performance testing. Experimental results on the database show that our approach is more consistent with ground truths than the latest Landsat 8 ACCA results.
AB - Cloud contamination is the most common defect leading to quality degradation in remote sensing images. Numerous cloud-cover assessment (CCA) methods have been developed in the literature. The traditional Landsat 7 CCA algorithm attempted to detect clouds by taking advantages of different spectral properties from five spectral bands. However, it suffers the weakness of omitting thin cirrus clouds and the requirement of thermal bands. In this paper, we derived an automated CCA (ACCA) model that measures statistical deviations in spatial domain between cloud and clear images. Moreover, it only conducts on panchromatic band image, which can successfully address the limitation of unavailable thermal bands for satellite missions without thermal infrared sensors on board. A database with 400 clear/cloud images is then built for performance testing. Experimental results on the database show that our approach is more consistent with ground truths than the latest Landsat 8 ACCA results.
KW - Cloud-cover assessment
KW - Natural scene statistic
KW - Spatial domain
KW - Thin cirrus/stratus cloud
UR - https://www.scopus.com/pages/publications/85041797202
U2 - 10.1109/IGARSS.2017.8128134
DO - 10.1109/IGARSS.2017.8128134
M3 - Conference contribution
AN - SCOPUS:85041797202
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5034
EP - 5037
BT - 2017 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 July 2017 through 28 July 2017
ER -