Abstract
In this paper, an image object identification problem for the Kaggle Iceberg Classifier Challenge was tackled by deep neural network. Basic convolutional neural network (CNN) was implemented and tested firstly. Then, deeper networks including VGG16 and ResNet50 are adopted to improve the accuracy. The deep learning-based methods are also compared with the conventional machine learning method i.e. SVM (support Vector Machine). Three feature augmentation approaches are utilized and compared, i.e. incidence angle confusion of satellite radar signals, multi-band composition and data augmentation of the original image data. Tentative results by GAN (Generative Adversarial Network) and Capsule Network are also presented. Results demonstrate the applicability and superiority of CNN over the conventional method (SVM) on the given dataset.
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Zhu, Y., Liu, C. (2020). Iceberg Detection by CNN Based on Incidence-Angle Confusion. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_6
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DOI: https://doi.org/10.1007/978-3-030-17798-0_6
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