:: Volume 3, Issue 2 (9-2015) ::
jgit 2015, 3(2): 1-19 Back to browse issues page
New Method and Indexes for Classification of Trees and Other Vegetation in Urban Area Using Aerial Visible Image
Mehrdad Eslami, Ali Mohammadzadeh *
K.N.Toosi University of Technology
Abstract:   (3327 Views)

Classification and detection of urban objects have always been a challenge for Photogrammetry and Remote Sensing researchers. Among various urban classes, plant complications due to their high diversity, spectral similarities in visible images and also absence of specific geometrical shapes have remarkable separation intricacies. In previous researches, for separation of the Trees class from "other vegetation" classes, different data sources have been used, which making use of visible images are the most low-cost and accessible data sources. Hence in this research study, an innovative method is developed for classification of Trees and "other vegetation" classes using visible image in urban areas. Therefore, firstly two new vegetation indices: Subdividing Vegetation Index (SVI) and Minus/ Subdividing Vegetation Index (MSVI), which are extracted from blue and green bands, are introduced. Then many textural features using Gray level Co-occurrence Matrix are estimated and then data reduction of Minimum Noise Fraction (MNF) is applied to the obtained textural features and first 5 bands had been selected. The obtained 2 new vegetation indices, 5 first bands of MNF and 3 bands of source images are entered to a Maximum Likelihood (MLL) classifier. The final result includes classification maps consist of Trees, "other vegetation" and "other urban objects" classes. The outcome of the newly proposed classification algorithm has shown the overall accuracy of 98.5 percent and Kappa coefficient values of about 93 percent. Furthermore, obtained results are shown the desirable effectiveness of the introduced vegetation indices in comparison to the other well-known vegetation indices for the classification overall accuracy, Kappa coefficient and average accuracy improvement. Also performance evaluation of the novel indices has shown an improvement about 4 percent in

Keywords: Seismotectonic zones, clustering, validity indexes, seismic data
Full-Text [PDF 931 kb]   (987 Downloads)    
Type of Study: Research |
Received: 2016/03/11 | Accepted: 2016/03/11 | Published: 2016/03/11



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Volume 3, Issue 2 (9-2015) Back to browse issues page