{'fraction_non_alphanumeric': 0.030326594090202177, 'fraction_numerical': 0.003110419906687403, 'mean_word_length': 5.668393782383419, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Island feature classification for single-wavelength airborne lidar bathymetry based on full-waveform parameters.

Because it is lightweight, low cost, and has high sampling density, single-wavelength airborne lidar bathymetry (ALB) is an ideal choice for shallow water measurements. However, due to severe waveform mixing, waveform classification has become the key difficulty in the research of single-wavelength ALB signal detection. Generally, the interaction between a laser and a water column leads to energy attenuation, pulse delay, or broadening of the water waveform, which has a discernible difference between terrestrial laser echo. This work attempts to focus on the morphology features in different waveforms to classify isolated, supersaturated, land, and water waveforms, and obtain a water-land division. The generalized Gaussian model optimized by the Levenberg-Marquardt algorithm (LM-GGM) is driven to extract 38-dimensional waveform parameters, covering different echo signals and their relationships. Ten-dimensional dominant features are selected from the feature matrix based on the random forest feature selection (RFFS) model, and input to the random forest classification model. Experiments show that the overall classification accuracy of the waveform is 97%.