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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Competitive Segmentation Performance on Near-Lossless and Lossy Compressed Remote Sensing Images
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Competitive Segmentation Performance on Near-Lossless and Lossy Compressed Remote Sensing Images

机译：近无损和有损压缩的遥感图像上的竞争性细分表现

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摘要

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it affects 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 (CRs). In some scenarios, accurate segmentation performance can be achieved even for high CRs.
机译：图像分割位于多个图像处理链的核心，并且实现了准确的分割是至高无上的最重要的。图像分割最近在遥感领域获得了兴趣，主要是由于遥感数据的广泛可用性。这种增加的可用性构成了发送和存储大量数据的问题。压缩是减轻这个问题的常见策略。然而，有损或近无损压缩可防止对恢复数据的完美重建。这封信调查了在近无损或有损压缩后重建的数据中的图像分割性能。从多个仪器的数据评估两个图像分割算法和两个压缩标准。实验结果表明，在低和中等压缩比（CRS）上没有显着降低了先前接近无损和有损压缩图像的分割性能。在某些情况下，即使高CRS也可以实现精确的分割性能。

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