Name
Abstract | ||
|---|---|---|
Coastline extraction in Synthetic aperture radar (SAR) images is a fundamental and challenging task due to the speckle noise. In this paper, we propose a new method for automatic coastline extraction in SAR images. In our method, we combine K-means and speckle noise removal methods together to increase the dissimilarity between sea and land. To enhance the robustness to speckle noise, and preserve the targets boundaries, we treat superpixels as basic regions instead of pixels in traditional pixel-based methods. Finally, an adaptive threshold is applied to classify these regions into sea or land. Based on the classifications, a canny detector is employed to detect the coastline. We evaluate our proposed method on SAR images and the improved coastline extraction method superpixel-based is verified on remote sensing images with RGB channels. The experimental results demonstrate its superior performance on coastline extraction. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7729262 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
Boundaries, K-means, Superpxiel, Coastline extraction, SAR images | Computer vision,Radar imaging,Speckle pattern,Computer science,Synthetic aperture radar,Remote sensing,Image segmentation,Robustness (computer science),Artificial intelligence,RGB color model,Pixel,Speckle noise | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.38 |
References | Authors | |
6 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofang Liu | 1 | 1 | 0.38 |
| Hong Jia | 2 | 1 | 0.38 |
| Liujuan Cao | 3 | 213 | 27.37 |
| Wang Cheng | 4 | 103 | 20.70 |
| Jonathan Li | 5 | 798 | 119.18 |
| Ming Cheng | 6 | 54 | 13.93 |