THE EFFECT OF JPEG2000 COMPRESSION ON REMOTE SENSING DATA OF DIFFERENT SPATIAL RESOLUTIONS

Anis Kamilah Hayati, Haris Suka Dyatmika

Abstract

The huge size of remote sensing data implies the information technology infrastructure to store, manage, deliver and process the data itself. To compensate these disadvantages, compressing technique is a possible solution. JPEG2000 compression provide lossless and lossy compression with scalability for lossy compression. As the ratio of lossy compression getshigher, the size of the file reduced but the information loss increased. This paper tries to investigate the JPEG2000 compression effect on remote sensing data of different spatial resolution. Three set of data (Landsat 8, SPOT 6 and Pleiades) processed with five different level of JPEG2000 compression. Each set of data then cropped at a certain area and analyzed using unsupervised classification. To estimate the accuracy, this paper utilized the Mean Square Error (MSE) and the Kappa coefficient agreement. The study shows that compressed scenes using lossless compression have no difference with uncompressed scenes. Furthermore, compressed scenes using lossy compression with the compression ratioless than 1:10 have no significant difference with uncompressed data with Kappa coefficient higher than 0.8.

Keywords

compression, effect, spatial resolution, remote sensing, JPEG2000

Full Text:

PDF

References

Bermejo S., Cabestany J., (2001), Oriented Principal Component Analysis for Large Margin Classifiers. Neural Networks, 14 (10), 1447–1461.

Cohen J., (1960), A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20, 37-46. doi: 10.1177/ 001316446002000104.

ISO/IEC 15444-1:2004, (2004) Information Technology – JPEG 2000 Image Coding System: Core Coding System. Geneva, Switzerland, 2004, 194.

ITU-T T.804, (2015), Information Technology - JPEG 2000 Image Coding System: Reference software. Geneva, Switzerland.

McEntee MF, Nikolovski I., Bourne R., et al., (2013), The Effect of JPEG2000 Compression on Detection of Skull Fractures. Academic Radiology. 20(6), 712-720.

McHugh ML, (2012), Interrater Reliability: The Kappa Statistic. Biochem. Med. 22, 276–282. doi: 10.11613/BM.2012.031

Shrestha B., O’Hara CG, Younan NH, (2005), JPEG2000: Image Quality Metrics. ASPRS 2005 Annual Conference.

Sung MM, Kim HJ, Yoo SK, et al., (2002), Clinical Evaluation of Compression Ratios using JPEG2000 on Computed Radiography Chest Images. Journal of Digital Imaging. 15(2):78-83.

Taubman DS, Marcellin MW, (2002), JPEG2000: Standard for Interactive Imaging. Proceedings of the IEEE. 90(8):1336-1357.

Zabala A., Cea C., Pons X., (2012b), Segmentation and Thematic Classification of Color Orthophotos over Non-Compressed and JPEG2000 Compressed Images. International Journal of Applied Earth Observation and Geoinformation Vol. 15.

Zabala A., Pons X., Diaz-Delgado R., et al., (2006), Effects of JPEG and JPEG2000 Lossy Compression on Remote Sensing Image Classification for Mapping Crops and Forest Areas. IEEE International Symposium on Geoscience and Remote Sensing. 781-784

Zabala A., Pons X., (2013), Impact of Lossy Compression on Mapping Crop Areas from Remote Sensing. International Journal of Remote Sensing. Vol. 34, No.8 2796-2813.

Zabala A., Vitulli R., Pons C., (2012a), Impact of CCSDS-IDC and JPEG 2000 Compression on Image Quality and Classification. Journal of Electrical and Computer Engineering, Vol. 2012, 2012.

Refbacks

  • There are currently no refbacks.