Neural network for LIDAR detection of fish

In this paper we present a neural network for detection of fish, from light detection and ranging (LIDAR) data and have described a classification method for distinguishing between water-layer, bottom and fish. Four multi-layer perceptrons (MLP) were developed for the classification purpose, where classes include fish, bottom and water-layer. The LIDAR data gives a sequence of intensity of laser backscatters obtained from laser shots at various heights above the Earth surface. The data is preprocessed to remove the high frequency noise and then a window of the sample is selected for further processing to extract features for classification purposes. We have used linear predictive coding (LPC) analysis for the feature detection purpose. The results show that the detection technique is effective and can do the required classification with a high degree of accuracy. We have tried our approach with four different MLPs and are presenting the data obtained from each of them.