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SVM and OBIA based Comparative Analysis on LANDSAT Multi Temporal Data for Wetland Mapping

Affiliations

  • Department of Civil engineering, SRM University, Kattankulathur, Kanchipuram − 603203, Chennai,Tamil Nadu,, India
  • Department of Civil engineering, SRM University, Kattankulathur, Kanchipuram − 603203, Chennai,Tamil Nadu, India

Abstract


Objectives: The Major objective of this study is to delineate the wetlands using the advanced image processing techniques and study the likelihood of these techniques for the mapping the real extent of wetlands on multi-temporal Lands at data. Methods/Statistical Analysis: The object based image analysis is based on the information of image objects rather than individual pixels. eCognition software is used for object oriented image analysis. The development of the objects and subsequent classification is achieved by multi resolution dissection using fuzzy logic approach1. The support vector machine is one of the supervised classification method is achieved by providing training sets. Findings: The study has been done from the year 1997 to 2014 for the changes in wetlands and their corresponding changes have been observed. The decrease in the areal extent of wetlands has been observed which is due to declining in the annual rainfall,population growth, rapid urbanization and industrialization over decades. It is observed that the classification by Object Based Image Analysis has outperformed the classification performed by SVM. The overall accuracy by this techniques is 76.01%, 75.02%, 77.80%, 76.83% for the data 1997, 2006, 2009, 2014 respectively while the accuracy is 94.2%, 93.2% ,93.8%, 93.1% respectively when classification is performed by object based image analysis. The areal extent of wetlands extracted by Support vector machine in Sq.km is 401.87, 381.96, 263.02, and 147.89 while they are 469.77, 406.46, 309.74 and 155.79 when extracted by object based image analysis for the year 1997, 2006, 2009 and 2014 respectively. Application/Improvements: The changes detected in wetlands over years can be used for the analysis of groundwater recharge, ecosystem & species growth etc.

Keywords

Fuzzy, Landsat, Multi-Temporal, Object Based Image Analysis, Support Vector Machine

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References


  • Jawak Shridhar D, Yogesh Palanivel V, Alvarinho J Luis. Semi-Automatic Extraction of Supra-Glacial Features using Fuzzy Logic Approach for Object Oriented Classification on WorldView-2 Imagery, Multispectral Hyper spectral and Ultra spectral Remote Sensing Technology Techniques and Applications VI, 98801S, April 30, 2016.
  • Lehner B, Döll P. Development and Validation of a Global Database of Lakes, Reservoirs and Wetlands. 2004; 96(1):1–22.
  • Finlayson CM, Spiers AG. Global Review of Wetland Resources and Priorities for Wetland Inventory. Supervising Scientist, Canberra, Australia.1999, p. 1-524.
  • Millennium Ecosystem Assessment (MEA) Ecosystems and Human Well-being: Wetlands and Water Synthesis.World Resources Institute, Washington, DC.2005, p. 1-82.
  • Secretariat R. The List of Wetlands of International Importance. The Secretariat of the Convention on Wetlands, Gland, Switzerland; 2013.
  • Space Applications Centre (SAC) National Wetland Atlas.SAC, Indian Space Research Organization, Ahmedabad.
  • Li J, Chen C. A Rule-Based Method for Mapping Cana da’s Wetlands using Optical, Radar and DEM Data, International Journal of Remote Sensing. 2005; 26(2):5051–69.
  • Shanmugam P, Ahn YH, Sanjeevi S. A Comparison of the Classification of Wetland Characteristics by Linear Spectral Mixture Modelling and Traditional Hard Classifiers on Multispectral Remotely Sensed Imagery in Southern India, Ecological Modelling. 2006; 194(4):379–94.
  • Frohn, RC, Autrey BC, Lane CR, Reif M. Segmentation and Object-Oriented Classification of Wetlands in a Karst Florida Landscape using Multi-Season Landsat-7 ETM+ Imagery, International Journal of Remote Sensing. 2011; 32(5):1471-89.
  • Haralick Statistical Image Texture Analysis, Handbook of Pattern Recognition and Image Processing. 1986; 11(6):247–80.
  • Cognition E. eCognition Developer (8.64.0) User Guide; Trimble Germany GmbH: Munich, Germany, 2010.
  • Dronova I. Object-Based Image Analysis in Wetland Research: A Review, Remote Sensing. 2015 ; 7(5) :6380413.
  • Baatz M, Schäpe A. Multiresolution Segmentation -An Optimization Approach for High Quality Multi-Scale Image Segmentation. 2000; 25(3):265-75.
  • Gupta N, Bhadauria B. Object based Information Extraction from High Resolution Satellite Imagery using eCognition, IJCSI International Journal of Computer Science Issues. 2014; 11(2):139-47.
  • Zadeh Z. Fuzzy Sets, Information and Control. 1965; 8(3):338–53.
  • Ursula C, Benz B, Hofmann P, Willhauck G, Lingenfelder G, Heynen M. Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information, ISPRS Journal of Photogrammetry and Remote Sensing, 2004; 58(3):239–58
  • Jabari S, Zhang Y. Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems, Algorithms.2013; 6(4):762-81.
  • Vapnik V. The Nature of Statistical Learning Theory. SpringerVerlag, New York, NY.1995.
  • Altaf A, Raeisi A. Presenting an Effective Algorithm for Tracking of Moving Object based on Support Vector Machine, Indian Journal of Science and Technology. 2015 Aug; 8(17):1-7.
  • Jayasri T, Hemalatha M. Categorization of Respiratory Signal using ANN and SVM based on Feature Extraction Algorithm, Indian Journal of Science and Technology. 2013 Sep; 6(9):1-6.
  • Karpagavalli S, Chandra E. A Hierarchical Approach in Tamil Phoneme Classification using Support Vector Machine, Indian Journal of Science and Technology. 2015 Dec; 8(35):1-7.
  • Kavitha R, Christopher T. An Effective Classification of Heart Rate Data using PSO-FCM Clustering and Enhanced Support Vector Machine, Indian Journal of Science and Technology. 2015 Nov; 8(30):1-9.
  • Pratiyush G, Manu S. Classifying Educational Data Using Support Vector Machines: A Supervised Data Mining Technique, Indian Journal of Science and Technology. 2016 Aug; 9(34):1-5.
  • Yaswanth V, Kumar KK, Harshith N, Teja GS, Aparna R.Outfit of Exemplar-SVMs for Object Detection and Beyond, Indian Journal of Science and Technology. 2016 Aug; 9(30):1-7.
  • Fauvel M, Chanussotand JA, Benediktsson B. Evaluation of Kernels for Multiclass Classification of Hyper Spectral Remote Sensing Data. In: Proceedings, IEEE International Conferenceon Acoustics, Speech, and Signal Processing, ICASSP, 2:II-II.2006.
  • Hsu CW, Chang CC, Lin CJ. A Practical Guide to Support Vector Classification. National Taiwan University; 2010. p.1-16.
  • Huang H. Use of Dark Object Concept and Support Vector Machines to Automate Forest Cover Change Analysis, Remote Sensing of Environment. 2008; 112(3):970–85.

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