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Article

A Cloud Detection Method for Landsat 8 Images Based on PCANet

by 1,2, 1,2,* and 1,2
1
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
2
Beijing Key Laboratory of Digital Media, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Received: 13 April 2018 / Revised: 29 May 2018 / Accepted: 1 June 2018 / Published: 5 June 2018
(This article belongs to the Special Issue Multispectral Image Acquisition, Processing and Analysis)
Cloud detection for remote sensing images is often a necessary process, because cloud is widespread in optical remote sensing images and causes a lot of difficulty to many remote sensing activities, such as land cover monitoring, environmental monitoring and target recognizing. In this paper, a novel cloud detection method is proposed for multispectral remote sensing images from Landsat 8. Firstly, the color composite image of Bands 6, 3 and 2 is divided into superpixel sub-regions through Simple Linear Iterative Cluster (SLIC) method. Then, a two-step superpixel classification strategy is used to predict each superpixel as cloud or non-cloud. Thirdly, a fully connected Conditional Random Field (CRF) model is used to refine the cloud detection result, and accurate cloud borders are obtained. In the two-step superpixel classification strategy, the bright and thick cloud superpixels, as well as the obvious non-cloud superpixels, are firstly separated from potential cloud superpixels through a threshold function, which greatly speeds up the detection. The designed double-branch PCA Network (PCANet) architecture can extract the high-level information of cloud, then combined with a Support Vector Machine (SVM) classifier, the potential superpixels are correctly classified. Visual and quantitative comparison experiments are conducted on the Landsat 8 Cloud Cover Assessment (L8 CCA) dataset; the results indicate that our proposed method can accurately detect clouds under different conditions, which is more effective and robust than the compared state-of-the-art methods. View Full-Text
Keywords: cloud detection; multispectral remote sensing; superpixel; PCA network; conditional random field cloud detection; multispectral remote sensing; superpixel; PCA network; conditional random field
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MDPI and ACS Style

Zi, Y.; Xie, F.; Jiang, Z. A Cloud Detection Method for Landsat 8 Images Based on PCANet. Remote Sens. 2018, 10, 877. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10060877

AMA Style

Zi Y, Xie F, Jiang Z. A Cloud Detection Method for Landsat 8 Images Based on PCANet. Remote Sensing. 2018; 10(6):877. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10060877

Chicago/Turabian Style

Zi, Yue, Fengying Xie, and Zhiguo Jiang. 2018. "A Cloud Detection Method for Landsat 8 Images Based on PCANet" Remote Sensing 10, no. 6: 877. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10060877

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