Waveform classification and information extraction from LIDAR data by neural networks

Two different neural network schemes for the classification of light detection and ranging (LIDAR) waveforms for the LARSEN 500 airborne system and for extraction of ocean information are proposed. The first method employs a single layer of linear neurons for classification of waveforms into various clusters. Both unsupervised and supervised learning algorithms have been employed to demonstrate the spatial distribution of milt in near-shore waters. In the second method, a new multistage multilayer feedforward architecture is used for the classification of the waveforms and for the extraction of various types of ocean information. The stage I networks work in a parallel fashion and map the input waveforms to a set of characteristics. The networks in stage II use these characteristics to assign a signature number to the waveform or extract other information. Both the schemes are used with real-world data collected by the LARSEN 500 system. The paper concludes with experimental results and comparisons.