Multitemporal Landsat Missing Data Recovery Based on Tempo-Spectral Angle Model

Multitemporal Landsat images play an important role in remote sensing applications. Unfortunately, missing data caused by cloud cover and sensor-specific problems have seriously limited its application. To improve the usability of Landsat data, several recovery methods have been proposed to fill the missing values. But, current studies mostly focus on spatial dimension and ignore the continuity of data in time dimension. More importantly, multitemporal images have more potential than single image in selecting similar pixels for recovering the missing pixels. In this paper, to recover missing pixels by jointly utilizing multispectral and multitemporal information, tempo-spectral angle mapping (TSAM) is proposed at first to measure tempo-spectral similarity between pixels described in spectral dimension and temporal dimension. Then, a multitemporal replacement method is used to recover missing data with the pixel selected by TSAM. Two new indices are also proposed to evaluate the effectiveness of TSAM. Simulated and actual multitemporal scanline corrector-off and cloud cover-Enhanced Thematic Mapper Plus images were used to assess the performance of our filling method. The quantitative evaluations suggest that the proposed method can predict the missing values accurately. The recovered results show that our method can keep the continuity of the boundary and is robust for the data with high percentage of missing.