Classification of Land-Water Continuum Habitats Using Exclusively Airborne Topobathymetric Lidar Green Waveforms and Infrared Intensity Point Clouds

Letard, Mathilde; Collin, Antoine; Corpetti, Thomas; Lague, Dimitri; Pastol, Yves; Ekelund, Anders

doi:10.3390/rs14020341

Cited by 15 publications

(12 citation statements)

References 49 publications

(76 reference statements)

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“…Their ease of use, efficiency, robustness to overfitting, generalization abilities and production of a feature importance metric (Breiman, 2001;Pal, 2007) explain their frequent use for 3D data classification. They have been used successfully in (Chehata et al, 2009;Hansen et al, 2021;Letard et al, 2022bLetard et al, , 2022a for point-based classifications of both topographic and TB lidar. In RF, since the decision trees are independent, one cannot compensate the potential weaknesses of another to improve the global performance of the forest.…”

Section: Features Classificationmentioning

confidence: 99%

“…As a result, vertically highly dense data is condensed into equally spaced punctual values, losing the spatial point pattern information. Few studies provide 3D classifications of underwater environments using bathymetric lidar (Hansen et al, 2021;Letard et al, 2022bLetard et al, , 2022a. Additionally, most of them require full-waveform information (Letard et al, 2022b(Letard et al, , 2022a, which is complex to process and often unavailable or unpublished.…”

Section: Introductionmentioning

confidence: 99%

“…Few studies provide 3D classifications of underwater environments using bathymetric lidar (Hansen et al, 2021;Letard et al, 2022bLetard et al, , 2022a. Additionally, most of them require full-waveform information (Letard et al, 2022b(Letard et al, , 2022a, which is complex to process and often unavailable or unpublished. On the land covers, TB lidar datasets offer potentially new classification opportunities using the bispectral backscattered intensity.…”

Section: Introductionmentioning

confidence: 99%

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3DMASC: accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric LiDAR data.

Letard¹,

Lague²,

Guennec

et al. 2023

Preprint

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<p>Three-dimensional data have become increasingly present in earth observation over the last decades and, more recently, with the development of accessible 3D sensing technologies. However, many 3D surveys are still underexploited due to the lack of accessible and explainable automatic classification methods. In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow. It handles multiple clouds at once, including or not spectral and multiple returns attributes. Through 3DMASC, we use classical 3D data multi-scale descriptors and new ones based on the spatial variations of geometrical, spectral and height-based features of the local point cloud. We also introduce dual-cloud features, encrypting local spectral and geometrical ratios and differences, which improve the interpretation of multi-cloud surveys. 3DMASC thus offers new possibilities for point cloud classification, namely for the interpretation of bi-spectral lidar data. Here, we experiment on topo-bathymetric lidar data, which are acquired using two lasers at infrared and green wavelengths, and feature two irregular point clouds characterized by different samplings of vegetated and flooded areas, that 3DMASC can harvest. By exploring the contributions of 88 features and 30 scales &#8211; including two types of neighborhoods &#8211; we identify a core set of features and scales particularly relevant for coastal and riverine scenes description, and give indications on how to build an optimal predictor vector to train 3D data classifiers. Our findings highlight the predominance of lidar return-based attributes over classical features based on dimensionality or eigenvalues, and the significant contribution of spectral information to the detection of more than a dozen of land and sea covers &#8211; artificial/vegetated/rocky/bare ground, rocky/sandy seabed, intermediate/high vegetation, buildings, vehicles, power lines. The experimental results show that 3DMASC competes with state-of-the-art methods in terms of classification performances while demanding lower complexity and thus remaining accessible to non-specialist users. Relying on a random forest algorithm, it generalizes and applies quickly to large datasets, and offers the possibility to filter out misclassified points depending on their prediction confidence. Classification accuracies between 91% for complex scene classifications and 98% for lower-level processing are observed, with average prediction confidences above 90% and models relying on less than 2000 samples per class and at most 30 descriptors &#8211; including both features and scales. Though dual-cloud features systematically outperform their single cloud equivalents, 3DMASC also performs on single cloud lidar data, or structure from motion point clouds. Our contributions are made available through a self-contained plugin in CloudCompare allowing non-specialist users to create a classifier and apply it, and an opensource labelled dataset of topo-bathymetric data.</p>

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Section: Features Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3DMASC: accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric LiDAR data.

Letard¹,

Lague²,

Guennec

et al. 2023

Preprint

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“…This type of research on aquatic plant habitats is presented by [ 124 , 125 ] in which machine learning techniques are used (Random Forest). The authors use single wavelength or bispectral barymetric lidar.…”

Section: Directions Of Bathymetric Lidar Developmentmentioning

confidence: 99%

The Use of Green Laser in LiDAR Bathymetry: State of the Art and Recent Advancements

Szafarczyk

Toś

2022

Sensors

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Bathymetric LiDAR technology is a technology used for simultaneous data acquisition regarding the morphology of the bottom of water reservoirs and the surrounding coastal zone, realized from the air, e.g., by plane or drone. Contrary to the air topographic LiDAR, which uses an infrared wavelength of 1064 nm, bathymetric LiDAR systems additionally use a green wavelength of 532 nm. The green laser can penetrate the water, which makes it possible to measure the depth of shallow water reservoirs, rivers, and coastal sea waters within three Secchi depths. This article presents the theoretical basis for the construction of a green laser. Against the background of other methods of measuring the bottom of water reservoirs, the technology using waves from the visible light range is presented in detail in the assessment of the bottom morphology of shallow water reservoirs. The possibilities of using green laser in lidar bathymetry implemented in particular in non-navigable regions are shown. The results of the researchers’ work on river processes (erosion, sedimentation), design of stream restoration, determination of morphometric parameters of the riverbed, as well as assessment of the topography of the marine coastal bottom zones are summarized. The development direction of lidar bathymetry is discussed.

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“…Light detection and ranging (LiDAR) is an active remote detection technique with application areas from military to civilian life, resolution and accuracy. The full-waveform height monitoring [7], canopy height retrieval [8], land cover classification [9], forest vegetation monitoring [10,11], building extraction [12].…”

Section: Introductionmentioning

confidence: 99%

Gaussian convolution decomposition for non-Gaussian shaped pulsed LiDAR waveform

Fang¹,

Wang²,

Zheng³

2022

Meas. Sci. Technol.

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The full waveform decomposition technique is significant for LiDAR ranging. It is challenging to extract the parameters from non-Gaussian shaped waveforms accurately. Many parametric models (e.g. the Gaussian distribution, the lognormal distribution, the generalized normal distribution, the Burr distribution, and the skew-normal distribution) were proposed to fit sharply-peaked, heavy-tailed, and negative-tailed waveforms. However, these models can constrain the shape of the waveform components. In this article, the Gaussian convolution model is established. Firstly, a set of Gaussian functions is calculated to characterize the system waveform so that asymmetric and non-Gaussian system waveforms can be included. The convolution result of the system waveform and the target response is used as the model for fitting the overlapped echo. Then a combination method of the Richardson–Lucy deconvolution, layered iterative, and Gaussian convolution is introduced to estimate the initial parameters. The Levenberg–Marquardt algorithm is used for the optimization fitting. Through experiments on synthetic data and practical recorded coding LiDAR data, we compare the proposed method with two decomposition approaches (Gaussian decomposition and skew-normal decomposition). The experiment results revealed that the proposed method could precisely decompose the overlapped non-Gaussian heavy-tailed waveforms and provide the best ranging accuracy, component fitting accuracy, and anti-noise performance. However, the traditional Gaussian and skew-normal decomposition methods can not fit the components well, resulting in inaccurate range estimates.

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Classification of Land-Water Continuum Habitats Using Exclusively Airborne Topobathymetric Lidar Green Waveforms and Infrared Intensity Point Clouds

Cited by 15 publications

References 49 publications

3DMASC: accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric LiDAR data.

3DMASC: accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric LiDAR data.

The Use of Green Laser in LiDAR Bathymetry: State of the Art and Recent Advancements

Gaussian convolution decomposition for non-Gaussian shaped pulsed LiDAR waveform

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