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Publicação
Palm tree image classification : a convolutional and machine learning approach
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 28.64 MB | Adobe PDF |
Autores
Resumo(s)
Convolutional neural networks have proven to excel at image classification tasks, do to
this they have being incorporated into the remote sensing field, initial hurdles in their
application like the need for large data sets or heavy computational burden, have being
solve with several approaches. In this paper the transfer learning approach is tested for
classification of a very high resolution images of a palm oil plantation. This approach
uses a pre trained convolutional neural network to extract features from an image,
and label them with the aid of machine learning models. The results presented in this
study show that the features extracted are a viable option for image classification with
the aid of machine learning models. An overall accuracy of 97% in image classification
was obtained with the support vector machine model.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Convolutional Neural Network Machine Learning Unnamed Aerial Vehicle Image Classification Transfer Learning OverFeat