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...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2017-07, Vol.55 (7), p.3656-3668
Main Authors: Gao, Guoming, Gu, Yanfeng
Format: Article
Language:English
Subjects:
Cloud cover
Clouds
Computer simulation
Data
Data processing
Data recovery
Earth
Extraterrestrial measurements
Image enhancement
Landsat
Landsat satellites
Mapping
Methods
Missing data
multitemporal images
Pixels
Recovering
Recovery
Remote sensing
Remote sensors
Satellite imagery
Satellites
Sensors
Similarity
similarity replacement
Spectra
tempo-spectral angle mapping (TSAM)
Thematic Mappers (LANDSAT)
Usability
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Summary: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 scan-line 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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2017.2656162