Hierarchical Sea-Land Segmentation for Panchromatic Remote Sensing Imagery

Long Ma, Nouman Q. Soomro, Jinjing Shen, Liang Chen*, Zhihong Mai, Guanqun Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Automatic sea-land segmentation is an essential and challenging field for the practical use of panchromatic satellite imagery. Owing to the temporal variations as well as the complex and inconsistent intensity contrast in both land and sea areas, it is difficult to generate an accurate segmentation result by using the conventional thresholding methods. Additionally, the freely available digital elevation model (DEM) also difficultly meets the requirements of high-resolution data for practical usage, because of the low precision and high memory storage costs for the processing systems. In this case, we proposed a fully automatic sea-land segmentation approach for practical use with a hierarchical coarse-to-fine procedure. We compared our method with other state-of-the-art methods with real images under complex backgrounds and conducted quantitative comparisons. The experimental results show that our method outperforms all other methods and proved being computationally efficient.

Original languageEnglish
Article number4728425
JournalMathematical Problems in Engineering
Volume2017
DOIs
Publication statusPublished - 2017

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