Title :
Classification of lidar waveforms by neural networks
Author :
Bhattacharya, D. ; Pillai, R. ; Antoniou, A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Victoria Univ., BC, Canada
Abstract :
A neural network scheme for the classification of lidar waveforms for the LARSEN 500 airborne system is proposed. It uses a single layer of linear neurons for classification of waveforms containing milt of various densities into a number of clusters. Both unsupervised and supervised learning algorithms have been employed to demonstrate the spatial distribution of milt in near-shore waters. The spatial distribution of waveforms obtained from real-world data provided by the LARSEN 500 system was found to be consistent with that obtained from observed data
Keywords :
aquaculture; geophysical signal processing; learning (artificial intelligence); neural nets; oceanographic techniques; optical radar; pattern classification; remote sensing by laser beam; unsupervised learning; LARSEN 500 airborne system; clusters; fish population; lidar waveform classification; linear neurons; near-shore waters; neural network scheme; sea-bed topography; spatial distribution; supervised learning algorithms; unsupervised learning algorithms; Clustering algorithms; Laser radar; Marine animals; Marine technology; Neural networks; Neurons; Oceans; Optical pulses; Optical reflection; Sea measurements;
Conference_Titel :
Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3073-0
DOI :
10.1109/ISCAS.1996.541595
