Abstract
This paper presents Artificial Neural Network (ANN) based architecture for underwater object detection from Light Detection And Ranging (Lidar) data. Lidar gives a sequence of laser backscatter intensity obtained from laser shots at various heights above the earth surface. Lidar backscatter can be broadly classified into three different classes: water-layer, bottom and fish. Multilayered Perceptron (MLP) based ANN architecture is presented, which employ different signal processing techniques at the data preprocessing stage. The Lidar data is pre-filtered to remove noise and a data window of interest is selected to generate a set of coefficient that acts as input to the ANNs. The prediction values obtained from ANNs are fed to a Support Vector Machine (SVM) based Inference Engine (IE) that presents the final decision.
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© 2005 Springer-Verlag Berlin Heidelberg
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Mitra, V., Wang, C., Banerjee, S. (2005). Lidar Signal Processing for Under-Water Object Detection. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_91
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DOI: https://doi.org/10.1007/11427445_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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