Effect of image compression and resampling methods on accuracy of land-cover classification

  • Mohamed Hosny
  • Hosam Elhabrouk
  • Aly Mohamed Elnaggar Faculty of Engineering Alexandria university
  • Hatem Taha
Keywords: Image compression, Image classification, MrSID, JPEG2000


High-resolution digital images that are used in remote sensing technologies tend to be of large sizes, especially with satellite imageries that provide better than 5 m spatial resolution such as (QuickBird, IKONOS, etc). These images consume larger storage space, larger transmission bandwidth, and longer transmission times. In order to minimize the storage space and transmission time, the archived images should be compressed before storage and transmission. Most publications generally investigate the quality of reconstructed (compressed) image, only few studies address the influence of the image compression on processing results, i.e. image classification. This paper investigates the effects of the most commonly used compression wavelet-based formats (JPEG2000 and MrSID) on the classification results of the two high-resolution QuickBird and IKONOS images. It is important to mention that the two high-resolution images cover two completely different study areas; therefore the study methodology was divided accordingly into two main cases. Both cases to the end that the impact of previously mentioned two wavelet-based formats was assessed on the classification results. Furthermore, only for the first data set, another effect was investigated. This other effect is the influence of the traditional resampling methods on the classification results. It is important to mention that typically, in the geo-referencing or geometric correction operations, resampling process is the second step following rectification. Moreover, the influence of resampling methods over compressed images was investigated. This paper shows that classification accuracy which derived from MrSID compressed images is better than that accuracy of JPEG2000 compressed images, especially at the high compression ratios of the two data sets. The first data set illustrate that the NN resampling method is the best method, in which the classification accuracy that was obtained was significantly closer to the classification accuracy of original image than any other traditional methods. Moreover, we concluded that CC method is the best in case of JPEG2000 compressed images while BI method is the best in case of MrSID, especially at high compression ratios. Meanwhile, the second data sets revealed that the ANNs classification method introduce classification accuracy higher than MLC method in case of MrSID compressed image, at higher compression ratios, while it was the higher in case of JPEG2000 at all compression ratios.


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Arun, P. V., 2013, A Visual mining based framework for classification accuracy estimation, Geodesy and Cartography, Vol. 62, No 2, pp. 113-121

Campbell, J.B., 1996, Introduction to Remote Sensing, 2nd ed., Guilford, New York.

DigitalGlobe, 2006, Quickbird imagery products – Product Guide, Revision 4.7.1, Digitalglobe Inc., 1 May 2006.

Dikshit, O. and Roy, D.P., 1996, An Empirical Investigation of image Resampling Effects upon the Spectral and Textural Supervised Classification of a High Spatial Resolution Multispectral image, Photogrammetric Engineering & Remote Sensing, Vol. 62, No. 9, pp. 1085-1092.

Fahim Arif, 2009, Level-3 Geometric Correction of FORMOSAT-2 Satellite Imagery and Efficient Image Resampling, Ph.D. thesis, National University of Sciences and Technology, Rawalpindi

GeoEye, 2006, IKONOS Imagery Products and Products Guide, Version 1.5, January 2006.

Katiyar, S.K. and Arun, P.V., 2014, Cellular Automata based adaptive resampling technique for the processing of remotely sensed imagery, American Society for Photogrammetry & Remote Sensing, Vol. 79 2b, pp. 182-193

Kiema, J. B. K., 2000b, Effect of wavelet compression on the automatic classification of urban environments using high resolution multispectral imagery and laser scanning data, International Archives of Photogrammetry and Remote Sensing, Amsterdam, Netherlands, Vol. XXXIII, Part B3, pp. 488–495.
Lam, K. W., Lau, W.L., and Li, Z.L., 2000, The effects on image classification using image compression technique, International Archives of Photogrammetry and Remote Sensing, Amsterdam, Vol. XXXIII, Part B7, pp. 744–751

LizardTech, 2010, LizardTech’s MrSID Technology, Celartem Inc. dba LizardTech, Seattle, Washington, USA.

Marsetic, A., Kokalj, Z., and Ostir, K., 2011, The effect of lossy image compression on object base image classification – WorldView -2 Case study, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Hannover, Germany, Vol. XXXVIII-4/W19, 2011 ISPRS Hannover 2011 Workshop, 14-17 June 2011, pp. 187-192.

Pillai, R. B. and Tueller, P. T., 2003, Scale Change Evaluations for Arid Land Image Interpretation – A Case Study at CAMP WILLIAMS, UTAH, 19th Biennial Workshop on Color Photography, Videography and Airborne Imaging for Resource Assessment, Logan, Utah

Ramesh, Venkateswarlu, N.B., and Murthy, J.V.R., 2013, A New Classification Performance Aware Multisensor, Multi Resolution Satellite Image Compression Technique, Global Journal of Computer Science and Technology,Graphics & Vision,Vol. 13, Issue 7,Version 1.0, pp. 13-24

Rodríguez, J. R., Miranda, D., and Álvarez, C. J., 2006, Application of satellite images to locate and inventory vineyards in the designation of origin “BIERZO” in SPAIN, Transactions of the (ASABE) American Society of Agricultural and Biological Engineers, Vol. 49, No. 1, pp. 277−290

Shrestha, B., O’Hara, G. C., and Younan, H. N., 2005, JPEG2000: Image quality metrics, ASPRS 2005 Annual Conference, Geospatial Goes Global: From Your Neighborhood to the Whole Planet, Baltimore, Maryland

Zabala, A. and Pons, X., 2011, Effects of lossy compression on remote sensing image classification of forest areas, International Journal of Applied Earth Observation and Geoinformation, Vol. 13, pp. 43–51

Zabala, A., Cea, C., and Pons, X., 2012b, Segmentation and thematic classification of color orthophotos over non-compressed and JPEG 2000 compressed images, International Journal of Applied Earth Observation and Geoinformation, Vol. 15, pp. 92-104