Recovering the large gaps in Landsat 7 SLC-off imagery using weighted multiple linear regression (WMLR)

Since 2003, the permanent failure of the scan line corrector (SLC) of the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor has seriously limited the scientific applications and usability of ETM+ data. While a number of methods have been conducted to fill the regular un-scanned locations in ETM+ SLC-off images, only a few researches have been developed to recover the large gap areas in such images. In this study, an innovative gap filling method has been introduced to reconstruct the large gap locations in SLC-off images via multi-temporal auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC-off image and two auxiliary fill images in parallel using the multiple linear regression (MLR) model in two successive steps. In the first step, almost half the gap locations have been recovered using the MLR model, then in the second step a weighted multiple linear regression (WMLR) algorithm is proposed to recover the remaining missing values. The simulated and actual case studies show that the proposed approach may provide a powerful tool for recovering the large gaps in SLC-off images, especially when there is a long time interval between the auxiliary fill images and the target SLC-off image.

[1]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

[2]  Colin Childs,et al.  Interpolating Surfaces in ArcGIS Spatial , 2004 .

[3]  Ghazali Sulong,et al.  Survey on gap filling algorithms in Landsat 7 ETM+ images , 2014 .

[4]  Liangpei Zhang,et al.  Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model , 2014 .

[5]  S. K. Maxwell,et al.  Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping , 2008 .

[6]  Ghazali Sulong,et al.  Recovering defective Landsat 7 Enhanced Thematic Mapper Plus images via multiple linear regression model , 2016, IET Comput. Vis..

[7]  Konstantinos G. Nikolakopoulos,et al.  Open quarry monitoring using gap-filled LANDSAT 7 ETM SLC-OFF imagery , 2014, Remote Sensing.

[8]  Feng Chen,et al.  Making Use of the Landsat 7 SLC-off ETM+ Image Through Different Recovering Approaches , 2012 .

[9]  J. Storey,et al.  LANDSAT 7 SCAN LINE CORRECTOR-OFF GAP-FILLED PRODUCT DEVELOPMENT , 2005 .

[10]  Peter L. Bonate,et al.  Pharmacokinetic-Pharmacodynamic Modeling And Simulation , 2005 .

[11]  Xue Wang,et al.  Research on Algorithms for Recovering Landsat - 7 Gap Data , 2011, 2011 International Conference on Control, Automation and Systems Engineering (CASE).

[12]  Thomas Alexandridis,et al.  Rapid error assessment for quantitative estimations from Landsat 7 gap-filled images , 2013 .

[13]  David P. Roy,et al.  Continuity of Landsat observations: Short term considerations , 2011 .

[14]  D. Civco,et al.  Application of geographically weighted regression to fill gaps in SLC-off Landsat ETM+ satellite imagery , 2014 .

[15]  Yi Wang,et al.  Universal reconstruction method for radiometric quality improvement of remote sensing images , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[16]  Valeria Rulloni,et al.  Large gap imputation in remote sensed imagery of the environment , 2010, Comput. Stat. Data Anal..

[17]  S. Franklin,et al.  Comparison of data gap-filling methods for Landsat ETM+ SLC-off imagery for monitoring forest degradation in a semi-deciduous tropical forest in Mexico , 2015 .

[18]  supM.A.K. Sadiq,et al.  Single and Multi-source Methods for Reconstruction the Gaps in Landsat 7 ETM+ SLC-off Images , 2015 .

[19]  Ghazali Sulong,et al.  Reconstruction the Missing Pixels for Landsat ETM+SLC-off Images Using Multiple Linear Regression Model , 2016 .

[20]  Michael Schmidt,et al.  Geostatistical interpolation of SLC-off Landsat ETM+ images , 2009 .

[21]  Liangpei Zhang,et al.  Sparse-based reconstruction of missing information in remote sensing images from spectral/temporal complementary information , 2015 .

[22]  Jin Chen,et al.  A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images , 2012 .

[23]  Feng Gao,et al.  A simple and effective method for filling gaps in Landsat ETM+ SLC-off images , 2011 .

[24]  Chao Zeng,et al.  Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method , 2013 .

[25]  Carlos Torres-Verdín,et al.  Efficient Numerical Simulation of Axisymmetric Electromagnetic Induction Measurements Using a High-Order Generalized Extended Born Approximation , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Zhang Chuanrong,et al.  SLC(走査線補正装置)オフLandsat ETM+(強化されたセマティックマッパー・プラス)衛星画像におけるギャップを埋める地理的重み付け回帰の応用 , 2014 .

[27]  Mazlan Hashim,et al.  Assessment of Landsat 7 Scan Line Corrector-off data gap-filling methods for seagrass distribution mapping , 2015 .

[28]  Weidong Li,et al.  Gaps‐fill of SLC‐off Landsat ETM+ satellite image using a geostatistical approach , 2007 .

[29]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Yi Jiang,et al.  A Comparison Study of Missing Value Processing Methods in Time Series Data Mining , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[31]  G. L. Schmidt,et al.  A multi‐scale segmentation approach to filling gaps in Landsat ETM+ SLC‐off images , 2007 .

[32]  Martin Kappas,et al.  Multi-Source Remotely Sensed Data Combination: Projection Transformation Gap-Fill Procedure , 2008, Sensors.

[33]  Sathit Prasomphan.  Imputing Landsat7 ETM+ with SLC-off image using the similarity measurement between two clusters , 2012, The First International Conference on Future Generation Communication Technologies.

[34]  Gang Yang,et al.  Missing Information Reconstruction of Remote Sensing Data: A Technical Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[35]  Kim F. Nimon,et al.  Interpreting Multiple Linear Regression: A Guidebook of Variable Importance , 2012 .