ABSTRACT

Remote sensing has become an essential technique in water environment monitoring. However, the cloud contamination of remote sensing images breaks its spatial and temporal continuity in practical application. The current cloud removal algorithms always based on the hypothesis that the ground surface has no significant changes temporally, which is not proper for optically complex water body. Therefore, to overcome the weakness of current cloud removal algorithms on temporally fast-changing landscapes, a temporal unmixing-based cloud removal (TUBCR) algorithm is proposed to estimate the under-cloud information of optically complex water images. In this algorithm, the spatial information and temporal information were separated into temporal abundance images and temporal endmembers. By extracting the high temporal resolution endmembers, the fast-changing water information were estimated. The performance of the proposed algorithm was compared with those of two widely used cloud removal algorithms: the modified neighbourhood similar pixel interpolator (mNSPI) algorithm and the weighted linear regression (WLR) algorithm. The results of four simulated datasets (500 images in total) and two real Geostationary Ocean Colour Imager (GOCI) datasets (8 images in total) show that the proposed algorithm performed better in both quantitative indexes and visual effect compared with the mNSPI algorithm and the WLR algorithm. In satellite image time series, the proposed algorithm could accurately estimate under-cloud water information and correct the offsets of the statistical temporal trends that caused by cloud contamination. Hence, theoretically, the proposed TUBCR algorithm altered the similar pixel-based strategy of classic methods and got a satisfied performance in fast-changing water images. In applications, the TUBCR algorithm has great potential to improve the spatiotemporal coverage of optically complex water remote sensing.

摘要

遥感已成为水环境监测中必不可少的技术。然而，在实际应用中，遥感图像的云污染破坏了其空间和时间的连续性。当前的除云算法始终基于以下假设：地面暂时没有明显变化，这不适用于光学复杂的水体。因此，为了克服当前的云去除算法在时间快速变化的景观上的弱点，提出了一种基于时间分解的云去除（TUBCR）算法来估计光学复杂水图像的云下信息。在该算法中，将空间信息和时间信息分离为时间丰度图像和时间结束成员。通过提取时间分辨率高的末端成员，估算了快速变化的水信息。将该算法的性能与两种广泛使用的云去除算法的性能进行了比较：改进的邻域相似像素插值器（mNSPI）算法和加权线性回归（WLR）算法。四个模拟数据集（总共500张图像）和两个真实地球静止海洋彩色成像仪（GOCI）数据集（总共8张图像）的结果表明，与mNSPI算法和WLR算法。在卫星图像时间序列中，所提出的算法可以准确地估计云层下的水信息，并纠正由云层污染引起的统计时间趋势的偏移。因此，从理论上讲 提出的TUBCR算法改变了经典方法中类似的基于像素的策略，并且在快速变化的水图像中具有令人满意的性能。在应用中，TUBCR算法具有很大的潜力，可以改善光学复杂的水遥感的时空覆盖范围。