Long‐term and short‐term analysis of shoreline change and cliff retreat on Brazilian equatorial coast

Leisner, Melvin Moura; de Paula, Davis Pereira; Alves, Deivid Cristhian Leal; da Guia Albuquerque, Miguel; de Holanda Bastos, Frederico; Vasconcelos, Yan Gurgel

doi:10.1002/esp.5668

Cited by 1 publication

(1 citation statement)

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“…The latter method is easy to use, and the equipment acquisition costs are relatively low. For these reasons, it has experienced a boom over the last decade [11][12][13][14][15][16][17]. Data acquired in this way always carry (in addition to spatial information) information on point color, typically in the form of three color components (red, green, and blue, as typically captured by standard cameras).…”

Section: Introductionmentioning

confidence: 99%

Color-Based Point Cloud Classification Using a Novel Gaussian Mixed Modeling-Based Approach versus a Deep Neural Network

Štroner,

Urban,

Línková

2023

Remote Sensing

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The classification of point clouds is an important research topic due to the increasing speed, accuracy, and detail of their acquisition. Classification using only color is basically absent in the literature; the few available papers provide only algorithms with limited usefulness (transformation of three-dimensional color information to a one-dimensional one, such as intensity or vegetation indices). Here, we proposed two methods for classifying point clouds in RGB space (without using spatial information) and evaluated the classification success since it allows a computationally undemanding classification potentially applicable to a wide range of scenes. The first is based on Gaussian mixture modeling, modified to exploit specific properties of the RGB space (a finite number of integer combinations, with these combinations repeated in the same class) to automatically determine the number of spatial normal distributions needed to describe a class (mGMM). The other method is based on a deep neural network (DNN), for which different configurations (number of hidden layers and number of neurons in the layers) and different numbers of training subsets were tested. Real measured data from three sites with different numbers of classified classes and different “complexity” of classification in terms of color distinctiveness were used for testing. Classification success rates averaged 99.0% (accuracy) and 96.2% (balanced accuracy) for the mGMM method and averaged 97.3% and 96.7% (balanced accuracy) for the DNN method in terms of the best parameter combinations identified.

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