Home   >   CSC-OpenAccess Library   >    Manuscript Information
Atmospheric Correction of Remotely Sensed Images in Spatial and Transform Domain
Priti Tyagi, Udhav Bhosle
Pages - 564 - 579     |    Revised - 01-11-2011     |    Published - 15-12-2011
Volume - 5   Issue - 5    |    Publication Date - November / December 2011  Table of Contents
Atmospheric Correction, Multispectral, Spatial Domain, Transform Domain, Vegetation Analyses, Regression
Remotely sensed data is an effective source of information for monitoring changes in land use and land cover. However remotely sensed images are often degraded due to atmospheric effects or physical limitations. Atmospheric correction minimizes or removes the atmospheric influences that are added to the pure signal of target and to extract more accurate information. The atmospheric correction is often considered critical pre-processing step to achieve full spectral information from every pixel especially with hyperspectral and multispectral data. In this paper, multispectral atmospheric correction approaches that require no ancillary data are presented in spatial domain and transform domain. We propose atmospheric correction using linear regression model based on the wavelet transform and Fourier transform. They are tested on Landsat image consisting of 7 multispectral bands and their performance is evaluated using visual and statistical measures. The application of the atmospheric correction methods for vegetation analyses using Normalized Difference Vegetation Index is also presented in this paper.
CITED BY (13)  
1 What carpet, M, happy, R., & Frydhsyny. (2016, February). The use of lidar technology to create a digital terrain model. In the First International Conference Third national conference on architecture, urban restoration and sustainable environment.
2 López-Serrano, P. M., Corral-Rivas, J. J., Díaz-Varela, R. A., Álvarez-González, J. G., & López-Sánchez, C. A. (2016). Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data. Remote Sensing, 8(5), 369.
3 Tyagi, P., & Bhosle, U. (2015). Kalman Filter Based Atmospheric Correction of Multispectral Images. Journal of Remote Sensing Technology, 3(2), 9.
4 Cai, L., Tang, D., & Li, C. (2015). An investigation of spatial variation of suspended sediment concentration induced by a bay bridge based on Landsat TM and OLI data. Advances in Space Research.
5 Soares, A. R., Candeias, A. L. B. O., & Sapucci, L. F. C. (2014). Avaliação da correção atmosférica em imagens orbitais utilizando dados de modelo de PNT.
6 Tyagi, P., & Bhosle, U. (2014). A New Comparative Study of Radiometric Correction on Satellite Images Using Kalman Filter and Levenberg Marquardt Algorithm. Advances in Image and Video Processing, 2(4), 53-65.
7 Khattab, M. F., & Merkel, B. J. (2014). Application of Landsat 5 and Landsat 7 images data for water quality mapping in Mosul Dam Lake, Northern Iraq. Arabian Journal of Geosciences, 7(9), 3557-3573.
8 Tyagi, P., & Bhosle, U. (2014). Radiometric correction of Multispectral Images using Radon Transform. Journal of the Indian Society of Remote Sensing, 42(1), 23-34.
9 Wang, Y., Hou, X., Jia, M., Shi, P., & Yu, L. (2014). Remote Detection of Shoreline Changes in Eastern Bank of Laizhou Bay, North China. Journal of the Indian Society of Remote Sensing, 42(3), 621-631.
10 Wang, Y., Hou, X., Shi, P., & Yu, L. (2013). Detecting shoreline changes in typical coastal wetlands of Bohai rim in North China. Wetlands, 33(4), 617-629.
11 Gyuris, M. P. wp7–demo-site implementation deliverable d. 7.3 report on rosia montana case study investigations–version 2.
12 Aziz, M. N., Salim, W. A., & SEKINE, M. Distribution, Utilization, and Society Perception of Mangrove of Coastal Areas in Rembang Regency, Indonesia.
13 GandhimathiaUsha, S., Vasuki, S., & Ariputhiran, G. (2012, December). Atmospheric correction of remotely sensed multispectral satellite images in transform domain. In Advanced Computing (ICoAC), 2012 Fourth International Conference on (pp. 1-5). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
Ahmet M., Eskicioglu and Paul S. Fisher, “Image Quality Measures and their performance”,IEEE Transactions on Communications, Vol. 43, No. 12, Dec 1995.
Bhosle, U. V., Pudale S., “Multivariate regression method for Radiometric correction of High resolution of Satellite data”, International Conference on Signal Processing,Instrumentation and Control, VIT, Pune.
Chavez, P.S. (1988). “An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data”. Remote Sensing of Environment, Vol. 24, pp.459-479.
Chavez, P.S. (1988). “Image Based atmospheric corrections- Revisited and Improved.”,Photogrammetric Engineering & remote sensing, Vol 62, No. 9, September 1996
Christopher R. Genovese and Larry Wasserman, “Confidence sets for nonparametric wavelet regression”. The Annals of Statistics, 2005, Vol. 33, No. 2, 698–729.
Huete, A. R. and Jackson, R. D., 1987, “Suitability of spectral indices for evaluating vegetation characteristics on arid rangelands”. Remote Sensing of Environment, 23, pp.213-232.
Janzen, D. T., Fredeen A. L.and Wheate,R. D., “Radiometric Correction techniques and accuracy assessment for Landsat TM data in remote forested regions”. Can. J. Remote Sensing, Vol 32, No. 5, pp. 330-340, 2006
Lillesand, T. M. and Kiefer, R. W. (1994), “Remote sensing and image linter predation”,John Wiley and sons Press
Rahman, H., and G. Dedieu. 1994. ”SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum”. International Journal of Remote Sensing,15(1):123-143.
Richter, R. 1990. “A fast atmospheric correction algorithm applied to Landsat TM images”.International Journal of Remote Sensing,11(1):159-166.
Richter, R. 1996. “A spatially adaptive fast atmospheric correction algorithm”. International Journal of Remote Sensing, 17(6):1201-1214
Robert E. Crippen, “Regression intersection method of adjusting Image data for band ratioing”, Int. J. Remote Sensing, 1987, Vol 8, no. 2, 137-155
Shunlin Liang, Hongliang Fang, and Mingzhen Chen, “Atmospheric Correction of Landsat ETM+ Land Surface Imagery—Part I: Methods”, IEEE Transactions On Geoscience And Remote Sensing, vol. 39, no. 11, November 2001, 2490-2498
Slater, P. N., Doyle, F. J., Fritz, N. L. and Welch R. (1983), “Photographic systems for remote sensing”, American Society of Photogrammetry Second Edition of manual of Remote Sensing, Vol. 1, Chap6. pp. 231-291
Mr. Priti Tyagi
PVPP College of Engineering - India
Dr. Udhav Bhosle
Rajiv Gandhi Institute of Technology - India