基于MODIS数据的青藏高原地表反射率重建方法研究

Research on method of surface reflectance reconstruction in the Tibetan Plateau based on MODIS data

青藏高原地表反射率在自然资源监测、生态环境保护和地球科学研究等方面有着重要应用。MOD09A1反射率数据由于云等因素的影响产生了大量异常像元,使得数据存在信息损失不完整的问题。考虑到邻近时序遥感影像具有高相关性,同类地物光谱具备高相似性,本文针对青藏高原地区提出了一种基于残缺多时相数据与地表覆盖分类信息的地表反射率深度学习重建方法。首先,以多时相MOD09A1反射率数据和MCD12Q1地表覆盖分类数据为基础,通过异常像元去除、有效图层提取、投影转换与拼接,得到目标区域基础反射率图像及辅助数据;其次,根据残差网络基本原理,构建了基于多时相数据与地表覆盖分类信息融合的深度学习网络模型;然后,利用MOD09A1数据完整区域裁剪的云掩膜样本、基于地表覆盖分类和K-means聚类算法生成的增广样本对模型进行训练;最后,将训练好的模型用于缺失数据区域地表反射率重建。通过两组对比试验表明,本文方法降低了对多时相辅助影像数据量和完整性的要求,在多时相数据残缺情况下,结合地表覆盖分类信息可实现对青藏高原大范围地表反射率的修复与重建。

The surface reflectance of the Tibetan Plateau is exploited in numerous applications,such as natural resource monitoring,ecological environmental protection,and geoscience research.Typically,the reflectance data of MOD09A1 are affected by detector noise and clouds,producing numerous abnormal pixels and diminishing the integrity and accuracy of remote sensing data.To address these issues,considering the universal geoscience law indicating that neighboring time-series remote sensing images are correlative,and the spectra of adjacent ground objects belonging to the same classification are similar,this paper proposes a deep learning method of surface reflectance reconstruction in the Tibetan Plateau based on incomplete multi-temporal data and land cover classification information.First,based on the multi-temporal reflectance data of MOD09A1 and land cover classification data of MCD12Q1,the basic reflectance image and auxiliary data of the target area are obtained through abnormal pixel removal,effective layer extraction,projection conversion,and mosaic.Subsequently,a deep learning network model is constructed based on the fusion of multi-temporal data and land cover classification information,according to basic principles of the residual network.Third,the deep learning model is trained using cloud mask samples cropped from an area with complete data and augmented training samples generated based on land cover classification and the K-means clustering algorithm.Finally,the trained model is utilized for surface reflectance reconstruction in the area with missing data.Two groups of comparative experiments demonstrate that the proposed method reduces the requirements for the amount and integrity of multi-temporal auxiliary image data and achieves accurate restoration and reconstruction of large-scale surface reflectance in the Tibetan Plateau by combining incomplete multi-temporal data and land cover classification information.