Automatic seagrass pattern identification on sonar images

Maryam Rahnemoonfar; Abdullah Rahman

doi:10.1117/12.2224191

12 May 2016 Automatic seagrass pattern identification on sonar images

Maryam Rahnemoonfar, Abdullah Rahman

Proceedings Volume 9844, Automatic Target Recognition XXVI; 98440C (2016) https://doi.org/10.1117/12.2224191
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Natural and human-induced disturbances are resulting in degradation and loss of seagrass. Freshwater flooding, severe meteorological events and invasive species are among the major natural disturbances. Human-induced disturbances are mainly due to boat propeller scars in the shallow seagrass meadows and anchor scars in the deeper areas. Therefore, there is a vital need to map seagrass ecosystems in order to determine worldwide abundance and distribution. Currently there is no established method for mapping the pothole or scars in seagrass. One of the most precise sensors to map the seagrass disturbance is side scan sonar. Here we propose an automatic method which detects seagrass potholes in sonar images. Side scan sonar images are notorious for having speckle noise and uneven illumination across the image. Moreover, disturbance presents complex patterns where most segmentation techniques will fail. In this paper, by applying mathematical morphology technique and calculating the local standard deviation of the image, the images were enhanced and the pothole patterns were identified. The proposed method was applied on sonar images taken from Laguna Madre in Texas. Experimental results show the effectiveness of the proposed method.

Citation Download Citation

Maryam Rahnemoonfar and Abdullah Rahman "Automatic seagrass pattern identification on sonar images", Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440C (12 May 2016); https://doi.org/10.1117/12.2224191

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