Building surface texture segmentation in urban remote sensing image using improved ORTSEG algorithm

Deng, Shupei; Zhang, Ye; Tian, Shu

doi:10.1109/igarss.2015.7326789

Cited by 1 publication

(1 citation statement)

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“…Texture blurring and unclear edges between objects can be a consequence of near-lossless and lossy compression. These effects can make image segmentation a major challenge, because of difficulties in detecting homogeneous regions or boundaries [7].…”

Section: Introductionmentioning

confidence: 99%

Competitive Segmentation Performance on Near-Lossless and Lossy Compressed Remote Sensing Images

Garcia-Sobrino

Pinho

Serra-Sagristà

2020

IEEE Geosci. Remote Sensing Lett.

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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 several 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.

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Section: Introductionmentioning

confidence: 99%