Lossy compression is preferred for many of applications; however, it is not preferred in the remote sensing community, because the use of lossy compression may change the features of remote sensing data. In this paper, we study the effect of lossy compression on two of the most common indices for vegetation feature extraction; Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). The study is performed over several Landsat ETM+ images, and our experimental results show that the different transformations used in lossy compression techniques exhibit different impacts on the reconstructed NDVI and/or NDWI. We have also observed that, for certain compression techniques, a low PSNR may represent more vegetation features. This work shows the recommended compression techniques related to Landsat image vegetation quantity. Results and discussion provide helpful guidelines for joint classification and compression of remote sensing images.

Date Published: 29 August 2016
PDF: 8 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100333H (29 August 2016); doi: 10.1117/12.2245083

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)