Classification of Airborne Laser Point Clouds and Optical Images Based on Fuzzy Evidence Theory

Zongze, 赵宗泽 Zhao; Hongtao, 王宏涛 Wang; Cheng, 王成 Wang; Shuangting, 王双亭 Wang; Chunyang, 王春阳 Wang

doi:10.3788/lop57.182803

Cited by 1 publication

(1 citation statement)

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“…Wang Xiaoyang et al used K-means clustering to realize high-precision sea-land classification of ocean, land, and inland water [9]. Hu Shanjiang, Liang Gang et al built the optimal model of convolutional neural network for land and water echo signals [10][11]. In 2023, our research group also proposed a water depth prediction model based on one-dimensional convolutional neural networks, verifying the feasibility and effectiveness of deep learning methods in water depth prediction [12].…”

Section: Introductionmentioning

confidence: 99%

An effective method for filtering laser bathymetry echo signals

Liu,

Li,

Liu

et al. 2024

International Conference on Signal Processing and Communication Security (ICSPCS 2024)

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Effective echo signal filtering is a waveform preprocessing step in airborne laser bathymetry. To address the low automation in effective echo filtering, we proposed an echo signal filtering method based on the K-means clustering algorithm. First, bathymetry signals were captured and saved as coarse signals using Matlab software. Subsequently, features such as maximum intensity, location of maximum intensity, and half-peak width were extracted from the coarse signals. Finally, K-means clustering algorithm was used to classify the samples, filtering out noise and abnormal pulse signals. The results show that K-means clustering is feasible for signal filtering, with noise signals filtering accuracy of 99% and abnormal pulse signals filtering accuracy of 95%, achieving standards comparable to manual filtering and significantly improving filtering efficiency. This method can further serve as a method for exploring preprocessing of a large amount of bathymetry signals.

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Section: Introductionmentioning

confidence: 99%