JPEG2000 encoding of images with NODATA regions for remote sensing applications

Abstract

The aim of this work is to, within the JPEG2000 framework, enhance the coding performance obtained for images that contain regions without useful information, or without information at all, here named as NODATA regions. In Geographic Information Systems (GIS) and in Remote Sensing (RS), NODATA regions arise due to several factors, such as geometric and radiometric corrections, atmospheric events, the overlapping of successive layers of information, etc. Most coding systems are not devised to consider these regions separately from the rest of the image, sometimes causing a loss in the coding efficiency and in the post-processing applications. We propose two approaches that address this issue; the first technique (Average Data Region, ADR) is carried out as simple pre-processing and the second technique (ShapeAdaptive JPEG2000, SA-JPEG2000) modifies the coding system to avoid the regions without information. Experimental results, performed on data from real applications and different scenarios, suggest that the proposed approaches can achieve, e.g., for SA-JPEG2000, a Signal-toNoise Ratio improvement of about 8 dB. Moreover, in a post-processing application such as a digital classification, the best classification results are obtained when the proposed approaches SA-JPEG2000 and ADR are applied.

Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study

“…Besides the above mentioned methods, there were also many others which have evaluated compression efficiency against the specific remote sensing tasks like classification [35,43,55,[57][58][59][60], image segmentation [61], atmospheric parameter retrieval [14,62], spectral unmixing [63], endmember extraction [64], vegetation [53,54] and spectral [65] indices computation, and biophysical variables estimation [66]. In [59] it was concluded that in the context of the general applications it is hard to define optimal level of image quality that should be preserved by the lossy compression.…”

Spatial pattern alterations from JPEG2000 lossy compression of remote sensing images: massive variogram analysis in high performance computing

“…Defining the nodata value as −999 is not arbitrary because nodata values that are defined too far from the valid data range can negatively affect normalization compression procedures. 38 Consistent treatment of nodata values is one of the keys to performing correct image processing and subsequent data analysis.…”