ABSTRACT

The Wide Field of View (WFV) imaging system equipped on the GaoFen-6 (GF-6) optical remote sensing satellite can acquire an image with a swath width 800 km and a resolution of 16 m, which is the largest Earth observation swath width among similar satellites in the world. With the advantages of the high spatial resolution and the wide field of view, GF-6 WFV images are widely used in agricultural resources monitoring, forestry resources investigation, and disaster relief. However, the existence of clouds is inevitable problem in GF-6 WFV images, which influences their availability. To quickly and accurately detect cloud areas in GF-6 WFV images, a cloud detection method for GF-6 WFV images based on the spectrum and variance of superpixels is proposed in the paper. First, the GF-6 WFV image is down-sampled. The simple linear iterative clustering algorithm is used to segment down-sampled images to obtain superpixels. The initial cloud detection result is obtained based on the spectrum of superpixels. Second, the initial cloud detection result is refined based on the variance of superpixels to eliminate the influence of cloud-like ground objects. Finally, the refined cloud detection result is post-processed using the region growing algorithm and expansion algorithm. The post-processed cloud detection result is up-sampled to obtain cloud detection result of the GF-6 WFV image. The experimental results show that the recall and precision of the proposed method are 84.61% and 88.46%, respectively, providing good cloud detection results for GF-6 WFV images.

摘要

高分六号（GF-6）光学遥感卫星搭载的宽视场（WFV）成像系统可获取幅宽800公里、分辨率16 m的图像，是目前地球观测幅宽最大的幅面图像。在世界同类卫星中。高分六号WFV影像具有空间分辨率高、视野广等优点，广泛应用于农业资源监测、林业资源调查、救灾等领域。然而，GF-6 WFV 图像中云的存在是不可避免的问题，这影响了它们的可用性。为了快速准确地检测GF-6 WFV图像中的云区域，提出了一种基于超像素光谱和方差的GF-6 WFV图像云检测方法。首先，GF-6 WFV 图像被下采样。简单的线性迭代聚类算法用于分割下采样图像以获得超像素。初始云检测结果是基于超像素的光谱获得的。其次，基于超像素的方差对初始云检测结果进行细化，以消除类云地物的影响。最后，使用区域增长算法和扩展算法对细化后的云检测结果进行后处理。对后处理的云检测结果进行上采样得到GF-6 WFV图像的云检测结果。实验结果表明，所提方法的召回率和准确率分别为84.61%和88.46%，为GF-6 WFV图像提供了良好的云检测结果。初始云检测结果是基于超像素的光谱获得的。其次，基于超像素的方差对初始云检测结果进行细化，以消除类云地物的影响。最后，使用区域增长算法和扩展算法对细化后的云检测结果进行后处理。对后处理的云检测结果进行上采样得到GF-6 WFV图像的云检测结果。实验结果表明，所提方法的召回率和准确率分别为84.61%和88.46%，为GF-6 WFV图像提供了良好的云检测结果。初始云检测结果是基于超像素的光谱获得的。其次，基于超像素的方差对初始云检测结果进行细化，以消除类云地物的影响。最后，使用区域增长算法和扩展算法对细化后的云检测结果进行后处理。对后处理的云检测结果进行上采样得到GF-6 WFV图像的云检测结果。实验结果表明，所提方法的召回率和准确率分别为84.61%和88.46%，为GF-6 WFV图像提供了良好的云检测结果。使用区域增长算法和扩展算法对细化后的云检测结果进行后处理。对后处理的云检测结果进行上采样得到GF-6 WFV图像的云检测结果。实验结果表明，所提方法的召回率和准确率分别为84.61%和88.46%，为GF-6 WFV图像提供了良好的云检测结果。使用区域增长算法和扩展算法对细化后的云检测结果进行后处理。对后处理的云检测结果进行上采样得到GF-6 WFV图像的云检测结果。实验结果表明，所提方法的召回率和准确率分别为84.61%和88.46%，为GF-6 WFV图像提供了良好的云检测结果。