Abstract
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.
Highlights
With the development of remote sensing technology, remote sensing images are widely used in land cover monitoring, environmental monitoring, geographic mapping and target recognizing and other fields [1,2,3,4,5]
A cloud detection framework is proposed for multispectral remote sensing images, which is implemented in MATLAB release 2016a on a computer with Intel CPU i7-6700K at 4.00 GHz and 32.00 GB RAM
In order to evaluate the effectiveness of the proposed framework, we compare it with three other frameworks, including the simple double-branch principal component analysis (PCA) Network (PCANet), the two-step superpixel classification strategy, and the two-step superpixel classification strategy combined with guided filter based refinement process
Summary
With the development of remote sensing technology, remote sensing images are widely used in land cover monitoring, environmental monitoring, geographic mapping and target recognizing and other fields [1,2,3,4,5]. A number of cloud detection methods have been proposed for optical remote sensing images. Hagolle et al [13] developed a Multi-Temporal Cloud Detection (MTCD) method using time series of images from Formosat-2 and Landsat, and the results indicate that the MTCD method is more accurate than the Automatic Cloud Cover Assessment method. Qian et al [15] designed an automated cloud detection algorithm named Mean Shift Cloud Detection (MSCD) using multi-temporal Landsat 8 Operational Landsat Imager (OLI) data without any reference images. These methods need more images over a short time period to ensure that the ground surface underneath does not change much. A clear reference image is difficult to acquire
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