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Title: Comparison of true-color and multispectral unmanned aerial systems imagery for marine habitat mapping using object-based image analysis
Authors: Papakonstantinou, Apostolos 
Stamati, Chrysa 
Topouzelis, Konstantinos 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Coastal remote sensing;Drone;Habitat mapping;Object-based image analysis (OBIA);UAS data acquisition;Unmanned aerial vehicle (UAV);Unmanned aircraft system (UAS)
Issue Date: 1-Feb-2020
Source: Remote Sensing, 2020, vol. 12, iss. 3
Volume: 12
Issue: 3
Abstract: The use of unmanned aerial systems (UAS) over the past years has exploded due to their agility and ability to image an area with high-end products. UAS are a low-cost method for close remote sensing, giving scientists high-resolution data with limited deployment time, accessing even the most inaccessible areas. This study aims to produce marine habitat mapping by comparing the results produced from true-color RGB (tc-RGB) and multispectral high-resolution orthomosaics derived from UAS geodata using object-based image analysis (OBIA). The aerial data was acquired using two different types of sensors-one true-color RGB and one multispectral-both attached to a UAS, capturing images simultaneously. Additionally, divers' underwater images and echo sounder measurements were collected as in situ data. The produced orthomosaics were processed using three scenarios by applying different classifiers for the marine habitat classification. In the first and second scenario, the k-nearest neighbor (k-NN) and fuzzy rules were applied as classifiers, respectively. In the third scenario, fuzzy rules were applied in the echo sounder data to create samples for the classification process, and then the k-NN algorithm was used as the classifier. The in situ data collected were used as reference and training data. Additionally, these data were used for the calculation of the overall accuracy of the OBIA process in all scenarios. The classification results of the three scenarios were compared. Using tc-RGB instead of multispectral data provides better accuracy in detecting and classifying marine habitats when applying the k-NN as the classifier. In this case, the overall accuracy was 79%, and the Kappa index of agreement (KIA) was equal to 0.71, which illustrates the effectiveness of the proposed approach. The results showed that sub-decimeter resolution UAS data revealed the sub-bottom complexity to a large extent in relatively shallow areas as they provide accurate information that permits the habitat mapping in extreme detail. The produced habitat datasets are ideal as reference data for studying complex coastal environments using satellite imagery.
ISSN: 20724292
DOI: 10.3390/rs12030554
Rights: © by the authors
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : University of the Aegean 
Appears in Collections:Άρθρα/Articles

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