The Effect of Jpeg2000 Compression on Remote Sensing Data of Different Spatial Resolutions

Abstract

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.

Qualitative Rating of Lossy Compression for Aerial Imagery by Neutrosophic WASPAS Method

“…As the quality of the compressed image depends not only on the compression algorithm but also on the compression ratio, four compression ratios were selected for each algorithm named as follows: low (25:1), medium (50:1), high (75:1), very high (100:1). The influence of the compression ratios lower than 25:1 is negligible, as shown in [19,31,34]. Higher compression ratios deteriorated image quality significantly and were not estimated in this work.…”

“…In [30], the effect of lossy compression was evaluated for edge detection, segmentation [31,32], and classification [32,33]. The visual features' degradation in areal images compressed using lossy algorithms is related to the image content and resolution [34]. The effect of lossy compression on the processed result and quality of the compressed images can be evaluated using subjective metrics like Mean Opinion Score (MOS), but it is not always effective.…”