Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images
García-Sobrino, Joaquín (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Pinho, Armando J. (University of Aveiro. Signal Processing Laboratory)
Serra Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)

Data:	2019
Resum:	Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it impacts later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from sev- eral instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios. In some scenarios, accurate segmentation performance can be achieved even for high compression ratios.
Ajuts:	Ministerio de Economía y Competitividad RTI2018-095287-B-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-463
Drets:	Tots els drets reservats.
Llengua:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Matèria:	Remote sensing data ; Image segmentation ; Lossy compression ; Near-lossless compression ; Maximum likelihood ; Successive band merging ; JPEG 2000 ; JPEG-LS
Publicat a:	IEEE geoscience and remote sensing letters, vol. 17 5 (August 2019) , ISSN 1545-598X

DOI: 10.1109/LGRS.2019.2934997

Postprint 6 p, 3.2 MB

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