Cloud Detection Method in GaoFen-2 Multi-spectral Imagery
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Cloud cover is one of the major factors which affect the application of GaoFen-2 imagery. Cloud detection in GaoFen-2 imagery is fairly difficult due to the lack of enough infrared bands. This paper presents a cloud detection method for GaoFen-2 multi-spectral imagery based on the radiation transmission model. The scattering coefficient of remote sensing image is estimated by using radiation transmission, and then the cloud mask was obtained by combining the geometric and texture features in high-resolution remote sensing images. Experiments on GaoFen-2 multi-spectral images show that the accuracy of cloud detection is above 94.70%. The method proposed in this paper can effectively reduce the influence of highlighted buildings during cloud detection and achieve a high accuracy for GaoFen-2 imagery cloud detection with less bands. In addition, this paper provides an alternative distinction method for the quantitative researches of thick and thin clouds in optical satellite imagery.
KeywordsCloud detection GaoFen-2 Radiation transmission Thick cloud Thin cloud
Optical sensing satellites are usually subject to clouds. Thin clouds affect the true land surface brightness, and thick clouds even completely obscure the ground which cause problems for applications of the optical remote sensing imagery, such as land cover classification  and land-use change , especially for quantitative studies, such as vegetation monitoring  and water detection . The distribution, thickness, and amount of cloud all restrict the use of satellite data, the accuracy of experiments, and the reliability of conclusions of researches in earth observation.
With the development of satellite sensors, various cloud detection methods have been developed for optical remote sensing images, such as cloud detection algorithms for NOAA advanced very high-resolution radiometer (AVHRR) satellite imagery ; for Terra/Aqua MODIS images [6, 7, 8]; for Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM)+ images [9, 10, 11]; for SPOT-5 high-resolution geometrical (HRG) imagery ; and for GaoFen-1 wide field of view (WFV) imagery .
Cloud detection methods can be divided into two categories: single-scene-based methods and multi-scene-based methods . Traditional thresholding methods are generally employed in single-scene-based cloud detection methods . The traditional thresholding approach can yield pleasing cloud detection results . However, it is an empirical cloud detection algorithm, and many coefficients need to be given. Therefore, low adaptability and universality are inevitable. In recent years, cloud detection schemes based on machine learning methods performed well, such as Markov random field (MRF) framework , support vector machine (SVM) , and deep learning . However, it is inevitable that computational complexity and time-consuming are high for machine learning approaches.
Multi-scene-based cloud detection methods usually utilize time-series images for comparison in short periods of time . Based on the hypothesis of invariance of surface features, change detection is used to determine whether it is a cloud or not, according to pixel-to-pixel contrast of spectral values and correlation parameters between multi-temporal remote sensing images. Multi-scene-based cloud detection method can also achieve great performance , but it is closely related to the reference image, as a ground truth.
The existing cloud detection approaches take cloud as the detection object, and clouds are extracted by using spectral parameters and texture features, such as the top-of-atmosphere (TOA) reflectance of Landsat OLI cirrus band  and the local binary pattern (LBP) . In this letter, the proposed method considers radiation features of the full scene, regards that clouds obscure the true ground and thus reduce the radiation transmission value of surface features. Based on the principle and methodology of image dehazing, the radiation transmission and scattering coefficient values are first estimated. Then, clustering method is used to extract the initial cloud pixels. The morphology method and texture feature analysis are combined to remove noises, and the final cloud mask is achieved.
The remaining of this paper is organized as follows: The proposed approach is presented in Sect. 2. The cloud detection process is introduced in Sect. 2.1. Error removal procedure is given in Sect. 2.2. The experimental results, analysis, and evaluation are presented in Sect. 3. Finally, the conclusion is given in Sect. 4.
2.1 Cloud Detection
2.1.1 Estimating Radiation Transmission Map Using Dark Channel Prior Method
In studies of image dehazing, the transmission is an important feature. The scene depth expresses the distance from an object to the camera. It is generally believed that the haze in the scene is uniformly covered, so the scattering coefficient can be assumed to be a constant. The greater the scene depth is, the thicker the haze is, and vice versa. Therefore, the estimation of scene depth can be used to restore haze-free images.
2.1.2 Generating a Rough Cloud Mask Using K-means Clustering Algorithm
2.2 Error Removal
2.2.1 Scattering Coefficient Modification Using Spectral Features
After these steps, a rough cloud mask by using spectral information is achieved. The condition for a cloud pixel is that it belongs to clustering results, and it is not water.
2.2.2 Error Removal Using Geometric and Texture Features
Moderate-to-high spatial resolution remote sensing images provide a great amount of details of land surface . Rich geographic objects increase the complexity of texture feature and thus generate many trivial errors. GaoFen-2 imagery lacks sufficient spectral information to use, and these errors are distinguishable in geometric and texture features from cloud patches. It is necessary to consider the texture and geometric morphometric information.
The strategy of geometric detection before texture detection can help to improve the operation efficiency since it is time-consuming for object-oriented texture features calculation, and geometric detection helps to significantly reduce the number of patches.
There are great differences on texture features between the cloudy region and the non-cloudy region in moderate to high-resolution remote sensing image. This paper applies graylevel and gradient information. Graylevel gradient co-occurrence matrix (GLGCM) is utilized to extract texture features comprehensively in this paper.
3 Experiments and Discussion
3.1 Data Set and Experimental Setup
To quantitatively evaluate the performance of the proposed method, ten GaoFen-2 high-resolution images were selected as experimental data, which were produced after relative radiometric correction and systematic geometric correction. The selected images, acquired from May 2015 to October 2018, have 7411 * 7025 pixels with multi-spectral (4 m/pixel) bands and four spectral channels (blue, green, red, and near-infrared).
3.2 Experiments and Evaluation
Test and parameter selections of the algorithm were carried out on local images. To test the effectiveness of the algorithm, we selected GaoFen-2 imageries which cover urban areas and rural areas and include thin and thick clouds with obvious or fuzzy boundaries. The cloud morphology includes planar thin clouds, massive thin clouds, planar thick clouds, massive thick clouds, and others.
Precision, recall, F-measure, and cloud coverages of ten cloud masks
In this paper, the presented cloud detection method for GF-2 multi-spectral imagery estimates the radiation transmission and scattering coefficient values, based on the methodology of image dehazing, and combines clustering method, morphology method, and texture feature analysis to achieve a cloud mask. The effectiveness of the algorithm and its superiority in the detection of thin clouds are proved by experiments and evaluation. How to overcome the accuracy of the algorithm in identifying small bright areas and distinguish different cloud thickness quantitatively will be our future researches.
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