Benthic habitat classification using multiscale GEOBIA on orthophoto images of Karimunjawa waters

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Yahya Dwikarsa
Abdul Basith

Abstract

The scale value is an important part of the segmentation stage which is part of Object-Based Image Analysis (OBIA). Selection of scale value can determine the size of the object which affects the results of classification accuracy. In addition to setting the scale value (multiscale), selection of machine learning algorithm applied to classify shallow water benthic habitat objects can also determine the success of the classification. Combination of setting scale values and classification algorithms are aimed to get optimal results by examining classification accuracies. This study uses orthophoto images processed from Unmanned Aerial Vehicle (UAV) mission intended to capture benthic habitat in Karimunjawa waters. The classification algorithms used are Support Vector Machine (SVM), Bayes, and K-Nearest Neighbors (KNN). The results of the classification of combination are then tested for accuracy based on the sample and Training Test Area (TTA) masks. The result shows that SVM algorithm with scale of 300 produces the best level of accuracy. While the lowest accuracy is achieved by using SVM algorithm with scale of 100. The result shows that the optimal scale settings in segmenting objects sequentially are 300, 200, and 100

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How to Cite
Dwikarsa, Y., & Basith, A. (2021). Benthic habitat classification using multiscale GEOBIA on orthophoto images of Karimunjawa waters. Communications in Science and Technology, 6(1), 55-59. https://doi.org/10.21924/cst.6.1.2021.332
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References

T. Blaschke, et al., Geographic object-based image analysis - Towards a new paradigm, ISPRS J. Photogramm. Remote Sens. 87 (2014) 180–191.

Y. Qian, W. Zhou, J. Yan, W. Li, dan L. Han, Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery, Remote Sens. 7 (2015) 153–168.

E. Casella, et al., Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques, Coral Reefs 36 (2017) 269–275.

D.G. Aguilera and P.R. Gonzalvez, Drones—An open access journal, Drones 1 (2017) 1.

P. Zinke and C. Flener, Experiences from the use of unmanned aerial vehicles (UAV) for river Bathymetry modelling in Norway innsendte artIkler, Vann 48 (2013) 351–360.

D. Ventura, A. Bonifazi, M.F. Gravina, A. Belluscio, G. Ardizzone, Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and Object-Based Image Analysis (OBIA), Remote Sens. 10 (2018) 1–23.

S.J. Campbell, Rebuilding management effectiveness in Karimunjawa marine national park, Packard Report 2006, WCS Asia Pacific Coral Reef Program, Bogor, Indonesia, 2006.

I. Asaad, C.J. Lundquist, M.V Erdmann, R.Van Hooidonk, M.J. Costello,Designating spatial priorities for marine biodiversity conservation in the Coral Triangle, Front. Mar. Sci. 5 (2018) 400.

S.J. Campbell, T. Kartawijaya, I. Yulianto, R. Prasetia, J. Clifton, Co-management approaches and incentives improve management effectiveness in the Karimunjawa National Park, Indonesia, Mar. Policy 41 (2013) 72–79.

W. Dwianasari, “Geomorphological spatial analysis for bentic habitat using pleiades image in part of Karimunjawa national park waters, BSc Thesis, Universitas Muhammadiyah Surakarta, Indonesia, 2017.

P.T. Harris and E.K. Baker, Why Map Benthic Habitats?, in Seafloor Geomorphology as Benthic Habitat, GeoHAB Atlas of Seafloor Geomorphic Features and Benthic Habitats, Elsevier, 2012.

L. Zhang, K. Jia, X. Li, Q. Yuan, X. Zhao, Multi-scale segmentation approach for object-based land-cover classification using high-resolution imagery, Remote Sens. Lett. 5 (2014) 73–82.

N. Wahidin, V.P. Siregar, B. Nababan, I. Jaya, Object-based image analysis for coral reef benthic habitat mapping with object-based image analysis for coral reef benthic habitat mapping with several classification algorithms, Procedia Environ. Sci. 24 (2015). 222–227.