A Convolutional Neural Network for Automatic Analysis of Aerial Imagery

Frederic Maire, Luis Mejias, Amanda Hodgson
2014 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)  
This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations
more » ... d for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.
doi:10.1109/dicta.2014.7008084 dblp:conf/dicta/MaireAH14 fatcat:vdchfzzsoreithow4gwamekyeq