Abstract
In this paper, we present methods for segmenting noisy two-dimensional forward-scan sonar images and classify and model their background. The segmentation approach differentiates the highlight blobs, cast shadows, and the background of sonar images. There is usually little information within relatively large background regions corresponding to the flat sea bottom and (or) water column, as they are often corrupted with speckle noise. Our experiments show that the background texture is dominated by the speckle noise which has the appearance of a pseudo-random texture. We show that the background texture of the underwater sonar images can be categorized by a small number of classes. The statistical features work better than the texture-based features in categorizing the pseudo-random background, which further strengthen our hypothesis of the dominance of noise over the background texture. As a result, we can model the noisy background with a few parameters. This has an application in coding the sonar images in which highlight blob regions and cast shadows are coded at the encoder side while the speckle noise-corrupted background can be synthesized at the decoder side. Since the background regions occupy a large fraction of the FS sonar image, we expect higher compression rates than most current image or video coding standards and other custom-designed sonar image compression techniques.
| Original language | English (US) |
|---|---|
| Title of host publication | OCEANS 2016 MTS/IEEE Monterey, OCE 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509015375 |
| DOIs | |
| State | Published - Nov 28 2016 |
| Event | 2016 OCEANS MTS/IEEE Monterey, OCE 2016 - Monterey, United States Duration: Sep 19 2016 → Sep 23 2016 |
Other
| Other | 2016 OCEANS MTS/IEEE Monterey, OCE 2016 |
|---|---|
| Country | United States |
| City | Monterey |
| Period | 9/19/16 → 9/23/16 |
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Keywords
- Forward-scan sonar imagery
- Sonar background classification
- Sonar image segmentation
- Speckle noise modeling and synthesis
ASJC Scopus subject areas
- Instrumentation
- Oceanography
- Ocean Engineering
Cite this
Segmentation, classification and modeling of two-dimensional forward-scan sonar imagery for efficient coding and synthesis. / Haghighat, Mohammad; Li, Xiuying; Fang, Zicheng; Zhang, Yang; Negahdaripour, Shahriar.
OCEANS 2016 MTS/IEEE Monterey, OCE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7761408.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Segmentation, classification and modeling of two-dimensional forward-scan sonar imagery for efficient coding and synthesis
AU - Haghighat, Mohammad
AU - Li, Xiuying
AU - Fang, Zicheng
AU - Zhang, Yang
AU - Negahdaripour, Shahriar
PY - 2016/11/28
Y1 - 2016/11/28
N2 - In this paper, we present methods for segmenting noisy two-dimensional forward-scan sonar images and classify and model their background. The segmentation approach differentiates the highlight blobs, cast shadows, and the background of sonar images. There is usually little information within relatively large background regions corresponding to the flat sea bottom and (or) water column, as they are often corrupted with speckle noise. Our experiments show that the background texture is dominated by the speckle noise which has the appearance of a pseudo-random texture. We show that the background texture of the underwater sonar images can be categorized by a small number of classes. The statistical features work better than the texture-based features in categorizing the pseudo-random background, which further strengthen our hypothesis of the dominance of noise over the background texture. As a result, we can model the noisy background with a few parameters. This has an application in coding the sonar images in which highlight blob regions and cast shadows are coded at the encoder side while the speckle noise-corrupted background can be synthesized at the decoder side. Since the background regions occupy a large fraction of the FS sonar image, we expect higher compression rates than most current image or video coding standards and other custom-designed sonar image compression techniques.
AB - In this paper, we present methods for segmenting noisy two-dimensional forward-scan sonar images and classify and model their background. The segmentation approach differentiates the highlight blobs, cast shadows, and the background of sonar images. There is usually little information within relatively large background regions corresponding to the flat sea bottom and (or) water column, as they are often corrupted with speckle noise. Our experiments show that the background texture is dominated by the speckle noise which has the appearance of a pseudo-random texture. We show that the background texture of the underwater sonar images can be categorized by a small number of classes. The statistical features work better than the texture-based features in categorizing the pseudo-random background, which further strengthen our hypothesis of the dominance of noise over the background texture. As a result, we can model the noisy background with a few parameters. This has an application in coding the sonar images in which highlight blob regions and cast shadows are coded at the encoder side while the speckle noise-corrupted background can be synthesized at the decoder side. Since the background regions occupy a large fraction of the FS sonar image, we expect higher compression rates than most current image or video coding standards and other custom-designed sonar image compression techniques.
KW - Forward-scan sonar imagery
KW - Sonar background classification
KW - Sonar image segmentation
KW - Speckle noise modeling and synthesis
UR - http://www.scopus.com/inward/record.url?scp=85006839994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006839994&partnerID=8YFLogxK
U2 - 10.1109/OCEANS.2016.7761408
DO - 10.1109/OCEANS.2016.7761408
M3 - Conference contribution
BT - OCEANS 2016 MTS/IEEE Monterey, OCE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -