Detection of Tree Crown from Satellite Imagery using Object Based Image Examination
Sujata R. Kadu1, Balaji G. Hogade2, Imdad Rizvi3

1Sujata R. Kadu, Department of Electronics and Telecommunication Engineering, Terna Engineering College, Navi Mumbai (Maharashtra), India.
2Balaji G. Hogade, Department of Electronics Engineering, Terna Engineering College, Navi Mumbai (Maharashtra), India.
3Imdad Rizvi, Department of Electrical Engineering, Division, Higher Colleges of Technology, Sharjah Campus, University City, Sharjah, United Arab Emirates.
Manuscript received on 18 September 2019 | Revised Manuscript received on 05 October 2019 | Manuscript Published on 11 October 2019 | PP: 281-285 | Volume-8 Issue-2S10 September 2019 | Retrieval Number: B10470982S1019/2019©BEIESP | DOI: 10.35940/ijrte.B1047.0982S1019
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Abstract: Detection and delineation of individual tree mainly depends on high resolution satellite images or LiDAR data. Urban green structure, specially urban trees plays a key role in enhancing the life of people. Now a day’s more than half of population is leaving in cities and urban areas. Methods to quantify and monitor trees are not efficient. The traditional methods for forest survey and ground survey are complex because of changes occurs in urban environment. The objective of this research is to extract vegetation using colour based and decision tree method, which can be further sub-classify to obtain area under tree canopy. The results obtained through Object-Based Image Analysis (OBIA) method are also compared with existing Gaussian Mixture Model (GMM) method. The overall accuracy achieved thereby is 93.85% using Decision tree-multiresolution segmentation and 93.31% using Decision tree-GMM method.
Keywords: Object Based Image Analysis, Decision Tree, Colour Based Segmentation, Gaussian Mixture Model, Multi Resolution Segmentation.
Scope of the Article: Image analysis and Processing