Segmenting mangrove ecosystems drone images using SLIC superpixels

Abstract Mangrove ecosystems play a very important ecological role on land–ocean interfaces in tropical regions. These ecosystems comprise of various tree species and aquatic animals, protecting the environment and providing a habitat that supports many living organisms including humans. The identification of image regions in mangrove ecosystems plays a significant role in ecosystem monitoring and conservation. Recent studies have suggested oversegmentation of colour images using superpixels as a solution to the segmentation of image regions. This study used the SLIC superpixel algorithm and k-means clustering to segment images taken from a camera mounted on a drone from a mangrove ecosystem in Fiji. The SLIC superpixel algorithm performed well to demarcate image regions with similar colour and texture information into patches and to use k-means for the segmentation of the whole image. These results lend support to the use of superpixel algorithms for the segmentation of mangrove ecosystems. Understanding how superpixels can be used for the segmentation of drone images will assist conservation efforts in mangrove ecosystems.

[1]  Chun-Yan Han,et al.  Improved SLIC imagine segmentation algorithm based on K-means , 2017, Cluster Computing.

[2]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[3]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[4]  Yong-Jin Liu,et al.  Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[8]  Hilde Bosmans,et al.  Automated implant segmentation in cone-beam CT using edge detection and particle counting , 2014, International Journal of Computer Assisted Radiology and Surgery.

[9]  Muditha K. Heenkenda,et al.  Mangrove tree crown delineation from high-resolution imagery , 2015 .

[10]  Bastian Leibe,et al.  Superpixels: An evaluation of the state-of-the-art , 2016, Comput. Vis. Image Underst..

[11]  Baohua Zhang,et al.  The study and application of the improved region growing algorithm for liver segmentation , 2014 .

[12]  Hemerson Pistori,et al.  Weed detection in soybean crops using ConvNets , 2017, Comput. Electron. Agric..

[13]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[17]  Jitendra Malik,et al.  Indoor Scene Understanding with RGB-D Images: Bottom-up Segmentation, Object Detection and Semantic Segmentation , 2015, International Journal of Computer Vision.

[18]  Benjamin W. Heumann.  An Object-Based Classification of Mangroves Using a Hybrid Decision Tree - Support Vector Machine Approach , 2011, Remote. Sens..

[19]  William F. Vásquez,et al.  An economic valuation of mangrove restoration in Brazil. , 2015 .

[20]  Linda G. Shapiro,et al.  A Review of Computer Vision Segmentation Algorithms , 2012 .

[21]  H. Hedberg.  A Survey of Various Image Segmentation Techniques , 2004 .

[22]  Zhiguo Cao,et al.  Image segmentation using modified SLIC and Nyström based spectral clustering , 2014 .

[23]  Xiaoming Xi,et al.  Performance evaluation of simple linear iterative clustering algorithm on medical image processing. , 2014, Bio-medical materials and engineering.

[24]  E. Barbier.  The protective service of mangrove ecosystems: A review of valuation methods. , 2016, Marine pollution bulletin.

[25]  Sajad Sabzi,et al.  Machine vision system for the automatic segmentation of plants under different lighting conditions , 2017 .

[26]  Etienne Belin,et al.  Simulation of image acquisition in machine vision dedicated to seedling elongation to validate image processing root segmentation algorithms , 2014 .

[27]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Yuzhen Lu,et al.  Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging , 2017 .

[29]  Antoine Collin,et al.  Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques , 2017, Coral Reefs.

[30]  Karen E. Joyce,et al.  Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia , 2016, J. Imaging.

[31]  Chen Shi,et al.  Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[32]  N. Duke,et al.  The Shoreline Video Assessment Method (S-VAM): Using dynamic hyperlapse image acquisition to evaluate shoreline mangrove forest structure, values, degradation and threats. , 2016, Marine pollution bulletin.

[33]  Zhengqin Li,et al.  Superpixel segmentation using Linear Spectral Clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Stefano Soatto,et al.  Quick Shift and Kernel Methods for Mode Seeking , 2008, ECCV.

[35]  Amaury Lendasse,et al.  Parameter-free image segmentation with SLIC , 2018, Neurocomputing.

[36]  Santiago Aja-Fernández,et al.  A local fuzzy thresholding methodology for multiregion image segmentation , 2015, Knowl. Based Syst..

[37]  Sanja Fidler,et al.  The Role of Context for Object Detection and Semantic Segmentation in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Lian Pin Koh,et al.  Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas , 2014 .

[39]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[40]  Giuseppe Salvo,et al.  Urban traffic analysis through an UAV , 2014 .

[41]  S. Süsstrunk,et al.  SLIC Superpixels ? , 2010 .

[42]  A D Belsare,et al.  Breast histopathology image segmentation using spatio‐colour‐texture based graph partition method , 2016, Journal of microscopy.

[43]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[44]  Paria Mehrani,et al.  Superpixels and Supervoxels in an Energy Optimization Framework , 2010, ECCV.

[45]  J. Senthilnath,et al.  Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods , 2017, Comput. Electron. Agric..

[46]  Davy Janssens,et al.  th EURO Working Group on Transportation Meeting , EWGT 2016 , 5-7 September 2016 , Istanbul , Turkey UAV-Based Traffic Analysis : A Universal Guiding Framework Based on Literature Survey , 2017 .