Proceedings ArticleDOI
Empirical study on building and tree detection
Pavan Malbhage,Suchitra Khoje +1 more
- pp 73-78
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TLDR
This study reviews latest advances in techniques used for detecting buildings and tress and finds Support Vector Machine (SVM) used for classification proved as best classifier for good accuracy.Abstract:
For urban modeling LiDAR images play very important role. High spatial resolution data and sophisticated methods are required by many data pre-processing techniques for LiDAR images for detecting buildings. In late decades, building and tree identification from LiDAR information and aerial images with high automation and precision level has been the center of numerous scientists which was chosen as the motivation behind this survey. This study reviews latest advances in techniques used for detecting buildings and tress. Support Vector Machine (SVM) used for classification proved as best classifier for good accuracy. The comparative study reveals positive and negative approach towards strategies used for detection.read more
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Proceedings ArticleDOI
Building and Tree Detection by Fusing LiDar and Aerial Images for Urban Development Planning
Pavan Malbhage,Suchitra Khoje +1 more
TL;DR: The relevant color and texture features are extracted from the images and given to support vector machine (SVM) for classification which successfully classifies buildings and trees from images with accuracy 96.96% and 59.01 % respectively.
References
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TL;DR: It is seen that relatively few features are needed to achieve the same classification accuracies as in the original feature space when classification of panchromatic high-resolution data from urban areas using morphological and neural approaches.
Journal ArticleDOI
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Journal ArticleDOI
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