Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea

Wang, Mingwei; Wu, Ziyin; Yang, Fanlin; Ma, Yi; Wang, Xiaohua; Zhao, Dineng

doi:10.3390/s18113828

Cited by 26 publications

(13 citation statements)

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“…The random selection of the input weights and deviations causes the ELM model to generate overfitting and instability problems [41], requiring a swarm intelligence algorithm to optimize its parameters. Common swarm intelligence algorithms include the ant colony algorithm [42], GA [27], and PSO [28]. In this paper, the PSO and RELM model are combined into the MELM classifier to optimize the weights and biases of the input layer (Figure 5).…”

Section: Relm Model and Pso Combined Into Melm Classifiermentioning

confidence: 99%

“…Meanwhile, swarm intelligence algorithms could be used to optimize parameters of classifiers. For example, a NN with a genetic algorithm (GA) [27] and an SVM with particle swarm optimization (PSO) [28] were used to classify the sediments in offshore areas and islands, achieving better classification results. However, the classification sensitivity of these techniques needs to be improved in order to distinguish more types of sediments and the small differences in category characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine

et al. 2020

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Acoustic backscatter data are widely applied to study the distribution characteristics of seabed sediments. However, the ghosting and mosaic errors in backscatter images lead to interference information being introduced into the feature extraction process, which is conducted with a convolutional neural network or auto encoder. In addition, the performance of the existing classifiers is limited by such incorrect information, meaning it is difficult to achieve fine classification in survey areas. Therefore, we propose a sediment classification method based on the acoustic backscatter image by combining a stacked denoising auto encoder (SDAE) and a modified extreme learning machine (MELM). The SDAE is used to extract the deep-seated sediment features, so that the training network can automatically learn to remove the residual errors from the original image. The MELM model, which integrates weighted estimation, a Parzen window and particle swarm optimization, is applied to weaken the interference of mislabeled samples on the training network and to optimize the random expression of input layer parameters. The experimental results show that the SDAE-MELM method greatly reduces mutual interference between sediment types, while the sediment boundaries are clear and continuous. The reliability and robustness of the proposed method are better than with other approaches, as assessed by the overall classification effect and comprehensive indexes.

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Section: Relm Model and Pso Combined Into Melm Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine

et al. 2020

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“…The predictive value of these variables is better than that of MBES data (i.e., bathymetric map and backscatter mosaic) as they provide the detailed information on seabed topography and substrata [51,52]. Among the various predictors, the majority of previous research typically used slope, curvature, eastness, and northness derived from bathymetric map and Gray Level Co-occurrence Matrix (GLCM) texture features [53][54][55][56][57] and Angular Range Analysis (ARA) parameters derived from backscatter mosaic [58][59][60]. Though bathymetric map has higher importance in modelling seagrass habitats [12,[61][62][63][64], other backscatter predictors may also contribute significantly to improve the model [62,65].…”

Section: Introductionmentioning

confidence: 99%

Seagrass Habitat Suitability Models using Multibeam Echosounder Data and Multiple Machine Learning Techniques

Muhamad

Hasan

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Seagrass beds are important habitats in the marine environment by providing food and shelter to dugongs and sea turtles. Protection and conservation plans require detail spatial distribution of these habitats such as habitat suitability maps. In this study, machine learning techniques were tested by using Multibeam Echo Sounder System (MBES) and ground truth datasets to produce seagrass habitat suitability models at Redang Marine Park. Five bathymetric predictors and seven backscatter predictors from MBES data were used to representing topography features and sediment types in the study area. Three machine learning algorithms; Maximum Entropy (MaxEnt), Random Forests (RF), and Support Vector Machine (SVM) were tested. The results revealed that MaxEnt and RF models achieved the highest accuracy (93% and 91%, respectively) with SVM produced the lowest (67%). Depth was identified as the most significant predictor for all three models. The contributions of backscatter predictors were more central for SVM model. High accuracy models showed that suitable habitat for seagrass is distributed around shallow water areas (<20 m) and between fringing reef habitats. The findings highlight that acoustic data and machine learning are capable to predict how seagrass beds are spatially distributed which provide important information for managing marine resources.

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“…Preston [8] used simulated annealing (SA) algorithms for sediment classification based on multi-beam data. Wang et al [9] compared the performances of different support vector machine (SVM) models on sediment classification tasks based on multi-beam and LiDAR data. Stewart et al [10] combined side-scan sonar imagery with Back Propagation neural networks (BPNNs) to classify sediments.…”

Section: Introductionmentioning

confidence: 99%

Sediment Classification of Small-Size Seabed Acoustic Images Using Convolutional Neural Networks

Luo

Qin

et al. 2019

IEEE Access

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Seabed acoustic images are image data mosaics derived from seafloor acoustic backscattering intensity data, which is related to the type of sediment covering the seabed. Therefore, submarine sediment classification can be realized using seabed acoustic images, and has been studied extensively. Recently, deep learning has also rapidly advanced; in particular, deep convolutional neural networks (CNNs) are now being used to achieve remarkable results in the field of image processing-showing that they are well-suited for image classification tasks. Previous studies have used GoogleNet to classify large-scale side-scan sonar data, with some sediments being well-classified. However, deep learning is data-driven and, theoretically, the greater the depth, the stronger is the learning ability of the feature. It is worth noting that the dataset used for sediment classification can sometimes be small. Hitherto, no related research has analyzed the feasibility and applicability of a CNN classifier for a small-sized seabed acoustic image dataset, so we adopted two different CNN classifier models to conduct the classification experiment in this study. As the results show, the CNN classifier can be applied to the classification of sediments based on a small-sized seabed acoustic image dataset, and the classification performance of shallow CNN was found to be better than that of the deep CNN on existing side-scan sonar data. In particular, the accuracy obtained from the results of several sediment classification experiments using a shallow CNN classifier ranged between 93.4% (Sand Wave) and 87.54% (Reef).INDEX TERMS Deep convolutional neural network, seabed acoustic image, seabed sediment classification.

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Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea

Cited by 26 publications

References 26 publications

Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine

Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine

Seagrass Habitat Suitability Models using Multibeam Echosounder Data and Multiple Machine Learning Techniques

Sediment Classification of Small-Size Seabed Acoustic Images Using Convolutional Neural Networks

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