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Advisor(s)
Abstract(s)
This paper exposes a study with the purpose of discriminating the different types of vegetation. One way to get information about the land is through cartography, where aerial photogrammetry is one of the most used techniques. The main objective of this study is the development of a tool capable of processing aerial photographs on the visible spectrum and for it to be able to discern and classify the different
types of vegetation.
The adopted methodology comprises into three major steps: feature extraction, feature selection and image
classification using two classifiers, K-Nearest Neighbors and Support Vector Machine. The first step extracts
statistical features and features of texture, the second step implements a technique that allows the selection of the most relevant features and the last step is divided in the
optimization of the classifiers input parameters and subsequent image classification. It was not possible to use the eight classes pre-defined due to the similarity between some of them, which led to the merge of some, resulting in four new classes. The images were classified according the new classes and the
performance of the two classifiers was compared. It was found that the best classifier is the Support Vector
Machine using the function of kernel Radial Basis Function showing 89,8% of correct classifications. The
influence of the feature selector was tested and it was concluded that it led to an average increase of 8,25% in the classifier’s performance. It was also concluded that the best results were achieved with 10 features for the KNearest Neighbors and with 20 features for the Support Vector Machine.
Description
Extended Abstract
Keywords
Aerial photography Classification Feature Selector Vegetation