Fast spatiotemporal data fusion: merging LISS III with AWiFS sensor data

The high resolution of remote sensors has evolved to capture the fine details of the Earth’s surface features in remote-sensing (RS) data. There is a trade-off between this fine spatial resolution and the temporal resolution of global space-borne sensors. Global space-borne sensors are not good enough to acquire an image at fine spatial and high temporal (FSHT) resolutions simultaneously. In this article, we propose a computationally efficient technique to create a FSHT resolution image using a ground-based data processing system. Resourcesat-2, part of the Indian Space Research Organization’s (ISRO’s) mission, carries linear imaging self-scanners (LISS III and LISS IV) and an advanced wide-field sensor (AWiFS) on board. The spatial and temporal resolutions of LISS III are 23.5 m and 24 days and those of AWiFS are 56 m and 5 days, respectively. The proposed method creates a synthetic FSHT resolution image with 23.5 m spatial and 5 day temporal resolution. This method is referred to as ‘LISS III spatial and AWiFS temporal’ (LSAT) data fusion. The LSAT data fusion method is based on a sub-pixel relationship between the images of a single AWiFS–LISS III image pair, which was acquired before or after the ‘prediction date’: a synthetic LISS III image for the time is ‘predicted’ (synthesized) using an AWiFS image at time and a single AWiFS–LISS III image pair at time , where . The LSAT model was tested on simulated and real data sets acquired by the LISS III and AWiFS sensors. The proposed method was compared with the recently developed spatiotemporal data fusion methods. The experimental results demonstrate that the method is computationally efficient and shows consistent prediction accuracy in retrieving surface reflectance changes.

[1]  C. Justice,et al.  Analysis of the phenology of global vegetation using meteorological satellite data , 1985 .

[2]  José A. Sobrino,et al.  Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco , 2000 .

[3]  Michael Elad,et al.  Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit , 2008 .

[4]  Ranganath R. Navalgund,et al.  The evolution of the earth observation system in India , 2012 .

[5]  Bo Huang,et al.  Spatiotemporal Reflectance Fusion via Sparse Representation , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Victor Haertel,et al.  Spectral linear mixing model in low spatial resolution image data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[7]  K. Beurs,et al.  Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology , 2012 .

[8]  Paul D. Gader,et al.  EK-SVD: Optimized dictionary design for sparse representations , 2008, 2008 19th International Conference on Pattern Recognition.

[9]  Joanne C. White,et al.  A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS , 2009 .

[10]  Bo Huang,et al.  Spatiotemporal Satellite Image Fusion Through One-Pair Image Learning , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[11]  J. Kerr,et al.  From space to species: ecological applications for remote sensing , 2003 .

[12]  Joanne C. White,et al.  Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. , 2009 .

[13]  D. L. Hall,et al.  Mathematical Techniques in Multisensor Data Fusion , 1992 .

[14]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[15]  Yong Du,et al.  Radiometric normalization, compositing, and quality control for satellite high resolution image mosaics over large areas , 2001, IEEE Trans. Geosci. Remote. Sens..

[16]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[17]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[18]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[19]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[20]  Xiaolin Zhu,et al.  An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .

[21]  Nicholas C. Coops,et al.  Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation , 2006 .

[22]  Gyanesh Chander,et al.  Evaluation and Comparison of the IRS-P6 and the Landsat Sensors , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[23]  E. Lambin,et al.  Dynamics of Land-Use and Land-Cover Change in Tropical Regions , 2003 .

[24]  P. Chavez Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[25]  Yun Zhang,et al.  Understanding image fusion , 2004 .

[26]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[27]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[28]  B. Holben Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[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]  Jindong Wu,et al.  Image-based atmospheric correction of QuickBird imagery of Minnesota cropland , 2005 .