The Comparison of Segmentation Results for UAV High-Resolution Remote Sensing Image among eCognition, ENVI and EDISON

Liu, Xin; Xu, Jingwen; Zhao, Junfang; Wu, Aoli; Ke, Jia-Hong; Liu, Xia; Huang, Yan-Ru; Zeng, Long-Wen

doi:10.12783/dtssehs/icss2016/9201

dtssehs

2017

DOI: 10.12783/dtssehs/icss2016/9201

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The Comparison of Segmentation Results for UAV High-Resolution Remote Sensing Image among eCognition, ENVI and EDISON

Xin Liu¹,

Jingwen Xu²,

Junfang Zhao³

et al.

Abstract: Abstract. The image segmentation is the basis of recognition and classification in digital image field. Choosing appropriate segmentation methods and software is necessary. The object-oriented software, eCognition and ENVI, and the software of EDISON based on Mean Shift algorithm, any of which can be used to segment image. This research segmented the pretreated UAV high-resolution sensing image by these software and compared the segmentation results. The result shows: The segmentation results effect of the sam… Show more

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Integrating Angular Backscatter Response Analysis Derivatives Into a Hierarchical Classification for Habitat Mapping

Porskamp¹,

Young²,

Rattray³

et al. 2022

Front. Remote Sens.

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Accurate maps of biological communities are essential for monitoring and managing marine protected areas but more information on the most effective methods for developing these maps is needed. In this study, we use Wilsons Promontory Marine National Park in southeast Australia as a case study to determine the best combination of variables and scales for producing accurate habitat maps across the site. Wilsons Promontory has full multibeam echosounder (MBES) coverage coupled with towed video, remotely operated underwater vehicle (ROV) and drop video observations. Our study used an image segmentation approach incorporating MBES backscatter angular response curve and bathymetry derivatives to identify benthic community types using a hierarchical habitat classification scheme. The angular response curve data were extracted from MBES data using two different methods: 1) angular range analysis (ARA) and 2) backscatter angular response (AR). Habitat distributions were predicted using a supervised Random Forest approach combining bathymetry, ARA, and AR derivatives. Variable importance metrics indicated that ARA derivatives, such as grain size, impedance and volume heterogeneity were more important to model performance than AR derivatives mean, skewness, and kurtosis. Additionally, this study investigated the impact of segmentation software settings when creating segmented surfaces and their impact on overall model accuracy. We found using fine scale segmentation resulted in the best model performance. These results indicate the importance of incorporating backscatter derivatives into biological habitat maps and the need to consider scale to increase the accuracy of the outputs to help improve the spatial management of marine environments.

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Integrating Angular Backscatter Response Analysis Derivatives Into a Hierarchical Classification for Habitat Mapping

Porskamp¹,

Young²,

Rattray³

et al. 2022

Front. Remote Sens.

View full text Add to dashboard Cite

show abstract

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The Comparison of Segmentation Results for UAV High-Resolution Remote Sensing Image among eCognition, ENVI and EDISON

Cited by 1 publication

References 3 publications

Integrating Angular Backscatter Response Analysis Derivatives Into a Hierarchical Classification for Habitat Mapping

Integrating Angular Backscatter Response Analysis Derivatives Into a Hierarchical Classification for Habitat Mapping

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