Seabed Sediment Classification Research Articles

Seabed Sediment Classification through Multispectral Backscatter Mosaic MBES and Angular Response Analysis

Seabed Sediment Classification through Multispectral Backscatter Mosaic MBES and Angular Response Analysis

Seabed Sediment Classification using Multi-Frequency MBES Bathymetric and Its Features

Principally, for a few decades, hydrographical surveys have been only to determine the depth of seawater. Sonar measurement tehcnology is the most widely used to conduct hydrographical surveys employing a singlebeam echosounder (SBES) and multibeam echosounder (MBES). In addition to depth information, seafloor sediment distribution maps are essential for port planning and management. In general, the distribution of seafloor sediments is predicted using backscatter data from SBES and MBES at single and multiple frequencies. The bathymetric data generated by the echosounder can be derived into several bathymetric features such as slope, ruggedness, roughness, aspect, bathymetric position index (BPI), and curvature. This study examines the possibility of using bathymetric measurement and bathymetric derivation of multi-frequency MBES to predict the distribution of seafloor sediments, especially in the harbor pond area. The study used a deep neural network (DDN) to classify the distribution of seabed sediments with bathymetric and bathymetric features input, validated with 74 in situ sediment samples (clayey sand, silt, sandy silt, and silty sand). Up to 75% of data sample sediments are used for training and 25% for validation. The classification results by DNN showed 42.6% clayey sand, 7.4% sandy silt, 46.7% silt, and 3.35% silty sand. The overall accuracy (AO) and Kappa classification of seabed sediments with DDN were 59.5% and 0.54 (moderate), respectively.

CNN Multibeam Seabed Sediment Classification Combined with a Novel Feature Optimization Method

CNN Multibeam Seabed Sediment Classification Combined with a Novel Feature Optimization Method

Research on Seabed Sediment Classification Based on the MSC-Transformer and Sub-Bottom Profiler

This paper proposed an MSC-Transformer model based on the Transformer’s neural network, which was applied to seabed sediment classification. The data came from about 2900 km2 of seabed area on the northern slope of the South China Sea. Using the submarine backscattering intensity and depth data obtained by the sub-bottom profiler, combined with latitude and longitude information, a seabed dataset of the slope area of the South China Sea was constructed. Moreover, using the MSC-Transformer, the accurate identification and judgment of sediment types such as calcareous bio-silt, calcareous bio-clay silt, silty sand, medium sand and gravel sand were realized. Compared with the conventional deep neural network CNN, RNN, etc., the model shows advantages when applied to the sediment dataset of the shallow sea slope region of the South China Sea. This confirms the feasibility and validity of the model and provides a reliable and accurate tool for seabed sediment classification in the field of marine science. The completeness and accuracy of the dataset and the good performance of the model provide a solid foundation for the scientificalness and practicability of the study.

Small-Sample Seabed Sediment Classification Based on Deep Learning

Seabed sediment classification is of great significance in acoustic remote sensing. To accurately classify seabed sediments, big data are needed to train the classifier. However, acquiring seabed sediment information is expensive and time-consuming, which makes it crucial to design a well-performing classifier using small-sample seabed sediment data. To avoid data shortage, a self-attention generative adversarial network (SAGAN) was trained for data augmentation in this study. SAGAN consists of a generator, which generates data similar to the real image, and a discriminator, which distinguishes whether the image is real or generated. Furthermore, a new classifier for seabed sediment based on self-attention densely connected convolutional network (SADenseNet) is proposed to improve the classification accuracy of seabed sediment. The SADenseNet was trained using augmented images to improve the classification performance. The self-attention mechanism can scan the global image to obtain global features of the sediment image and is able to highlight key regions, improving the efficiency and accuracy of visual information processing. The proposed SADenseNet trained with the augmented dataset had the best performance, with classification accuracies of 92.31%, 95.72%, 97.85%, and 95.28% for rock, sand, mud, and overall, respectively, with a kappa coefficient of 0.934. The twelve classifiers trained with the augmented dataset improved the classification accuracy by 2.25%, 5.12%, 0.97%, and 2.64% for rock, sand, mud, and overall, respectively, and the kappa coefficient by 0.041 compared to the original dataset. In this study, SAGAN can enrich the features of the data, which makes the trained classification networks have better generalization. Compared with the state-of-the-art classifiers, the proposed SADenseNet has better classification performance.

Mixed Seabed Sediment Classification Based on Transferred Convolutional Neural Network: A Case Study in the Ancient River Valley

The coastal zone is connected to the hinterland of the basin and the wide sea, which is not only affected by complex natural factors, but also by human activities. The study of sediment classification in this area will help to further explain topographic evolution and dynamic mechanism. Aiming at the complex mixed sediment environment in the ancient valley area of shallow seas, an advanced deep convolutional neural network classification model based on transfer learning—selective kernel hybrid dilated ResNet-50 (SKHD-ResNet-50) is thus proposed and applied to the coastal area in the Firth of Forth, Scotland, UK. The model overcomes problems of low accuracy of sediment classification with small samples, while effectively improving the efficiency of mixed sediment classification. Five transferred convolutional neural network models including CNN, AlexNet, VGG-19, GoogleNet, and SKHD-ResNet-50, were used to conduct seabed sediment classification experiments on multibeam backscatter mosaics in the study area. An ablation study about the effect of selective kernel convolution (SKC) and hybrid dilated convolution (HDC) was also conducted to further prove the usability of the proposed method. Finally, the optimized convolutional neural network was used to build a multi-layer deep learning model for sediment classification, combining the transfer learning method with the neural network to obtain the optimal network weights through deep global optimization, thus accurately mining statistical characteristics and distribution rules of backscatter intensity data. The experimental results show that the advanced model obtains better classification accuracy than that of state-of-art methods, with overall accuracy and Kappa coefficient of 96.87% and 0.9482, respectively. Findings highlight the proposed sediment classification model, which can effectively distinguish mixed sediments, and greatly improve the accuracy and efficiency of sediment classification.

A Sample Enhancement Method Based on Simple Linear Iterative Clustering Superpixel Segmentation Applied to Multibeam Seabed Classification

Full-coverage and high-efficiency seabed sediment detection and identification are critical elements of digital marine construction that support the three-dimensional and thematic development of maritime spatial geographic information systems. With the development of multibeam echo sounder (MBES), the use of MBES backscatter intensity and bathymetry data to extract backscatter angular response (AR) features has increased. Using backscatter intensity features and seabed terrain features for classification is an effective way to achieve large-scale seabed sediment classification. However, it was still limited by small sample size problems and the poor stability of the classification model due to the complexity of performing seabed sediment sampling. In response to the above issues, this paper proposes a sample enhancement method based on simple linear iterative clustering (SLIC) superpixel segmentation to address the problems. First, a superpixel-based sample homogeneity expansion method is combined with multibeam backscatter intensity images to achieve adaptive sample range selection. Then, a Random Forest (RF)-based model is constructed using MBES backscatter intensity and seabed terrain features. To assess the model’s validity, the experiment uses data from an extensive MBES survey and field sampling information from the Celtic Sea, UK. It achieves accurate predictions for the area’s eight sediment types. The experimental results show that the proposed method expands 60 groups of original sample points to 9293 groups of valid sample points, and achieves an overall classification accuracy and kappa coefficient of 84.05% and 0.81 for the seabed sediment, respectively. In addition, the classification accuracy is significantly better compared to the traditional methods.

Seabed sediments classification based on side-scan sonar images using dimension-invariant residual network

Seabed sediment classification has important applications in ocean engineering and submarine pipeline design. This paper proposed the dimension-invariant residual network to improve the seabed sediments classification accuracy. The dimension of each channel of the feature extracted from the dimension-invariant residual block is the same as the original image. Compared with down-sampling, each feature vector has more feature parameters, which can improve the accuracy of seabed sediments classification. Shortcut connections were introduced in the proposed network, which can make the network easier to optimize and achieve good performance by using only a not very deep network. It also has a lower complexity compared to powerful classifiers like Transformer and has a better performance in small-scale seabed sediment classification. And to improve the accuracy of seabed sediment classification without consuming too much time for training the classifier, the network parameters optimization method was proposed in this paper. Recording the classification accuracy and training time of the network with these different parameters. Determining the parameters of the network by the proposed optimization method. The proposed classification model and optimization method were validated by using the side-scan sonar images, acquired from the area around the Shandong Peninsula of China. The layers and convolutional kernel sizes were determined to be 8 and 3, respectively. The classification accuracy of overall, rock, sand, and mud of the dimension-invariant residual network model were 97.83%, 94.04%, 97.80%, and 98.02% respectively. In comparison with state-of-the-art classifiers, the experimental results demonstrated that the proposed classification model achieved better performance, and the network parameters optimization method enables the network to achieve satisfactory classification accuracy while spending less time. The proposed classification model and network parameters optimization method show great potential in seabed sediments classification, which can also be extended to the task of underwater target recognition in future research.

MBES Seabed Sediment Classification Based on a Decision Fusion Method Using Deep Learning Model

High-precision habitat mapping can contribute to the identification and quantification of the human footprint on the seafloor. As a representative of seafloor habitats, seabed sediment classification is crucial for marine geological research, marine environment monitoring, marine engineering construction, and seabed biotic and abiotic resource assessment. Multibeam echo-sounding systems (MBES) have become the most popular tool in terms of acoustic equipment for seabed sediment classification. However, sonar images tend to consist of obvious noise and stripe interference. Furthermore, the low efficiency and high cost of seafloor field sampling leads to limited field samples. The factors above restrict high accuracy classification by a single classifier. To further investigate the classification techniques for seabed sediments, we developed a decision fusion algorithm based on voting strategies and fuzzy membership rules to integrate the merits of deep learning and shallow learning methods. First, in order to overcome the influence of obvious noise and the lack of training samples, we employed an effective deep learning framework, namely random patches network (RPNet), for classification. Then, to alleviate the over-smoothness and misclassifications of RPNet, the misclassified pixels with a lower fuzzy membership degree were rectified by other shallow learning classifiers, using the proposed decision fusion algorithm. The effectiveness of the proposed method was tested in two areas of Europe. The results show that RPNet outperforms other traditional classification methods, and the decision fusion framework further improves the accuracy compared with the results of a single classifier. Our experiments predict a promising prospect for efficiently mapping seafloor habitats through deep learning and multi-classifier combinations, even with few field samples.

Deep-Sea Seabed Sediment Classification Using Finely Processed Multibeam Backscatter Intensity Data in the Southwest Indian Ridge

In 2007, China discovered a hydrothermal anomaly in the Longqi hydrothermal area of the Southwest Indian Ridge. It was the first seabed hydrothermal area discovered in the ultraslow spreading ocean ridge in the world. Understanding the types of seabed sediments in this area is critical for studying the typical topography and geological characteristics of deep-sea seabed hydrothermal areas. The traditional classification of deep-seabed sediments adopts box sampling or gravity column sampling and identifies the types of seabed sediments through laboratory analysis. However, this classification method has some shortcomings, such as the presence of discrete sampling data points and the failure of full-coverage detection. The geological sampling in deep-sea areas is particularly inefficient. Hence, in this study, the EM122 multibeam sonar data collected in the Longqi hydrothermal area, Southwest Indian Ridge, in April 2019 are used to analyze multibeam backscatter intensity. Considering various errors in the complex deep-sea environment, obtaining backscatter intensity data can truly reflect seabed sediment types. Through unsupervised and supervised classification, the seabed sediment classification in the Longqi hydrothermal area was studied. The results showed that the accuracy of supervised classification is higher than that of unsupervised classification. Thus, unsupervised classification is primarily used for roughly classifying sediment types without on-site geological sampling. Combining the genetic algorithm (GA) and the support vector machine (SVM) neural network, deep-sea sediment types, such as deep-sea clay and calcareous ooze, can be identified rapidly and efficiently. Based on comparative analysis results, the classification accuracy of the GA-SVM neural network is higher than that of the SVM neural network, and it can be effectively applied to the high-precision classification and recognition of deep-sea sediments. In this paper, we demonstrate the fine-scale morphology and surface sediment structure characteristics of the deep-sea seafloor by finely processing high-precision deep-sea multibeam backscatter intensity data. This research can provide accurate seabed topography and sediment data for the exploration of deep-sea hydrothermal resources and the assessment of benthic habitats in deep-sea hydrothermal areas.

Seabed Sediment Classification Using Spatial Statistical Characteristics

Conventional sediment classification methods based on Multibeam Echo System (MBES) data have low accuracy since the correlation between features and sediment has not been fully considered. Moreover, their poor resistance to the residual error of MBES backscatter strength (BS) processing also degrades their performances. Toward these problems, we propose a seabed sediment classification method using spatial statistical features extracted from angular response curve (ARC), topography, and geomorphology. First, to reduce interference of noise and residual error of beam pattern correction, we propose a robust method combining the Generic Seafloor Acoustic Backscatter (GSAB) model and Huber loss function to estimate the parameters of ARC which is strongly correlated with seabed sediments. Second, a feature set is constructed by AR features composed of GSAB parameters, BS mosaic and its derivatives, and seabed topography and its derivatives to characterize seabed sediments. After that, feature selection and probability map acquisition are employed based on the random forest algorithm (RF). Finally, a denoising and final sediment map generation method is proposed and applied to probability maps to obtain the sediment map with reasonable sediment distribution and clear boundaries between classes. We implement experiments and achieve the classification accuracy of 93.3%, which verifies the validity of our method.

Deep-Sea Sediment Mixed Pixel Decomposition Based on Multibeam Backscatter Intensity Segmentation

The ability to accurately map the seabed sediments plays an important role in seabed habitat development and stakeholder decision-making. In conventional seabed sediment classification methods, maps of seabed sediment are provided in categorical form (sediment classes). Therefore, the prediction of the sediment compositions in multibeam observational units has become a difficult issue in using conventional methods. To tackle this challenge, a new strategy is developed to realize the subpixel decomposition of seabed sediments. A key attribute of the proposed sediment decomposition model is that it utilizes spatial–spectral information provided by multibeam backscatter angular responses (ARs). First, an AR feature extraction method utilizing a bidirectional sliding window is proposed and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering algorithm is used for segmentation. Second, a deep-sea sediment decomposition model based on the fuzzy method is constructed by selecting experimental samples that are distributed within a single clustering region. This model inverts the abundance of each sediment composition in the form of membership degrees. Finally, deep-sea multibeam survey data collected from the central Philippine Sea are used for verification. The overall mean square error and coefficient of determination reach 0.043 and 0.856, respectively. The experimental results show that the new method can accurately decompose deep-sea sediment compositions, thus providing a new technique for deep-sea acoustic sediment remote sensing and quantitative analysis.

Seabed Sediment Classification for Sonar Images Based on Deep Learning

Along with the development of sonar technology, the detection accuracy and stability of sonar have been improved. A large amount of seabed sediment information can be obtained through sonar detection. However, this information is often accompanied by noise interference, resulting in poor quality of the generated images. Moreover, sonar images are different from conventional images. There are single-channel images. The model needs to classify the images according to the texture features in the images. Coupled with the scarcity of sonar data, this makes it difficult to accurately classify the seabed sediment. According to the characteristics of sonar images, we propose the ShuffleNet-DSE which is a classification model based on deep learning. The ShuffleNet-DSE network is based on ShuffleNet-V2, while ensuring the lightweight of the network, it incorporates feature dense connection and Squeeze-and-Excitation (SE) structure channel self-attention. And combined with the sonar image's characteristics, the partial activation function of the model is changed to the Swish. The experimental results show that compared with the traditional machine learning classification method, ShuffleNet-DSE has greatly improved the classification accuracy and the computational cost. Compared with excellent neural network models such as AlexNet, MobileNet-V3, GoogLeNet and ResNet, it is more suitable for sonar image processing.

DNN-based seabed classification using differently weighted MBES multifeatures

Seabed sediment classification has significance for the utilization of marine resources and marine scientific research. Currently, the multibeam echo sounder (MBES) is increasingly becoming the tool of choice for large-scale seabed sediment classification. To further explore the technology of seabed sediment classification, this paper proposes a new classification method. In addition to backscatter mosaic, the method also integrates three other different types of features, including texture features of backscatter mosaic, MBES bathymetry features, and backscatter angular response (AR) features, which are given different weights in the classification process. First, geographically weighted regression (GWR) analysis is performed between different types of features and seabed sediment types, and the normalized coefficient of determination (R2) is employed as the weight coefficient for the different types of features. Second, the backscatter mosaic is combined with features from different types to predict the seabed sediment types using a deep neural network (DNN) classifier. Third, the classification residuals of the features from these three different types are acquired through the above classification results. Last, the classification residuals of features from different types are added to the classification results of the backscatter mosaic according to the weights, thereby achieving seabed sediment classification based on MBES multifeatures with different weights. The results show that the overall classification accuracy of the seabed sediments can be significantly improved from 88.98%/85.14% to 93.43% when using the DNN classification model based on MBES multifeatures with different weights compared with the other two models (DNN classification model based on MBES multifeatures with equal weights and DNN classification model based on principal component analysis (PCA) dimensionality reduction). The kappa coefficient can also be significantly improved from approximately 0.85/0.80 to 0.91. Via analysis, the proposed method can reasonably assign the weights of the different features and take advantage of integrating MBES multifeatures for seabed sediment classification. This approach also provides an important reference for future research on seabed sediment classification.

A Novel Acoustic Sediment Classification Method Based on the K-Mdoids Algorithm Using Multibeam Echosounder Backscatter Intensity

The modern discrimination of sediment is based on acoustic intensity (backscatter) information from high-resolution multibeam echo-sounder systems (MBES). The backscattering intensity, varying with the angle of incidence, reveals the characteristics of seabed sediment. In this study, we propose a novel unsupervised acoustic sediment classification method based on the K-medoids algorithm using multibeam backscattering intensity data. In this method, we use the Lurton parameters model, which is the relationship between the backscattering intensity and incidence, to obtain the backscattering angle corresponding curve, and we use the genetic algorithm to fit the curve by the least-squares method. After extracting the four relevant parameters of the model when the ideal fitting effect was achieved, we input the characteristic parameters obtained from the fitting to the K-medoids clustering model. To validate the proposed classification method, we compare it with the self-organizing map (SOM) neural network classification method under the same parameter settings. The results of the experiment show that when the seabed sediment category is less than or equal to 3, the results of the K-medoids algorithm and the SOM neural network are approximately identical. As the sediment category increases, the SOM neural network shows instability, and it is impossible to see the clear boundaries of the seabed sediment, while the K-medoids category is 5 and the seabed sediment classification is correct. After comparing with field in situ seabed sediment sampling along the MBES survey line, the sediment classification method based on K-medoids is consistent with the distribution of the field sediment sampling. The classification accuracies for bedrock, sandy clay, and silty sand are all above 90%; those for gravel and clay are nearly 80%, and the overall accuracy reaches 89.7%.

Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm

Folk's textual classification scheme which is widely used for sediment study operates with the proportions of gravel, sand, silt and clay fractions conventionally. However, dealing with data from different sources usually needs to face missing values that may make the classification difficult. To solve this problem and discover other methods of analyzing the scheme, with samples of offshore seabed sediment, a two-stage model was established to predict a sample's class using the XGBoost algorithm as well as the grain size parameters as input features. The final model was evaluated with quantitative performance measures of recall, precision and F1 score, and by comparing sediment texture maps using the predicted and the actual data. The results show that the model performs well on extraction of sediment samples without gravel fraction, and prediction of classes that have independent characteristics of grain size parameters or samples not near the boundaries of classes in the ternary diagram. The predicted sediment texture is close to the actual and could be reliable due to errors with little impact on further applications. It is demonstrated that the model could be an auxiliary or alternative approach to offshore sediment texture mapping, as well as supplementary to the analysis of sedimentary environment.

Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning

Acoustic seabed classification (ASC) is a fast and large-scale seabed sediment survey method. In particular, combining it with an automated classifier can theoretically achieve fast automatic seabed sediment classification. However, owing to the cost of sampling, a lack of labeled data for sediment classification based on seabed acoustic images impedes the training and deployment of classifiers. Herein, we use shallow-water, side-scan sonar images collected from the Pearl River Estuary combined with deep learning to study sediment classification and optimization methods for a small dataset of seabed acoustic images. In this paper, we applied different and deeper convolutional neural networks (CNNs) and used grayscale CIFAR-10 for pretraining to achieve large-span parameter migration and improve model performance. The best result in the experiment is a 3.459% error rate achieved by ResNet after fine tuning, verifying the improvement brought by our fine tuning strategy and the deeper models used in such tasks. The results of data enhancement based on generative adversarial networks (GANs) indicated that this method can improve the accuracy of sediment classification; however, the effects of GANs are limited and they are computationally expensive. Overall, our findings resolve, to an extent, the dilemma of using small datasets of seabed acoustic images for sediment classification and provide a framework for future studies on sediment classification, which has a certain significance in helping people better understand the seabed.

A Novel Sub-Bottom Profiler Seabed Sediment Classification Method Based on BPNN With Biot-Stoll Model and Attenuation-Based Model

The exploitation and utilization of seabed sediments provide vital significance in many fields. Recently, t he classification of seabed sediments using sub-bottom profiler(SBP) data has become a research focus. Specifically, SBP data can be applied not only for recognizing individual stratigraphic layers but also for classifying seabed sediments by inversion models. To improve the sorting effect of gravel and mud simultaneously, we propose a sediment classification method based on aback propagation neural network (BPNN) with the Biot-Stoll model and an attenuation-based model. In this method, two datasets of the mean grain size derived from these two models are trained through the BPNN classifier to classify seabed sediments. The proposed method is verified through SBP data and in-situ sampling data collected from the sea north of the Shandong Peninsula, China. The experimental results show that the overall accuracy of sediment classification is 89.4%, and the classification accuracy of gravel and mud reach 91.4% and 93.3%, respectively, confirming that gravel and mud can be more accurately distinguished based on the proposed method than the single Biot-Stoll model and the single attenuation-based model.

Deep learning model for seabed sediment classification based on fuzzy ranking feature optimization

Accurate acquisition of information on seabed sediment distributions plays an important role in the construction of basic marine geographic databases. Although a multibeam echo-sounding system (MBES) can satisfy large-scale seafloor mapping with high precision and high resolution, the development of a consistent, stable, repeatable and validated seabed sediment classification method based on swath acoustic data is still in its infancy. To achieve accurate prediction and mapping of geographic seabed sediment information, this paper developed a deep learning model based on feature optimization. First, faced with high-dimensional features extracted from multibeam bathymetry and backscatter intensity measurement data, a fuzzy ranking (FR) feature optimization method was proposed. By combining the physical properties of actual sediment samples, the multidimensional features derived from terrain and intensity data are ranked and optimally selected according to the mean square error to eliminate redundant and irrelevant features. Second, the deep belief network (DBN) deep learning method was used to build a supervised seabed sediment classification model. The optimized features and actual sediment samples participate in model training, which further enhances the prediction ability of acoustic data to seabed sediments. Finally, to evaluate the performance of the DBN model, this experiment used large-scale multibeam survey data and ground-truth data (acquired by grabbers, core samplers, dredges, etc.) in the southern Irish Sea to achieve accurate prediction of 10 sediment types (slightly gravelly muddy sand, slightly gravelly sand, gravelly mud, gravelly muddy sand, gravelly sand, muddy sand, muddy sandy gravel, sand, sandy gravel and sandy mud). The experiment results show that by using the optimal feature combination based on FR, the overall classification accuracy and Kappa coefficient reached 86.20% and 0.834, respectively, which are significantly improved compared to the evaluation metrics of other feature selection methods. In addition, compared with the current five typical supervised classification methods (i.e., the random forests, BP neural network, support vector machine, maximum likelihood and decision trees methods), the proposed DBN classification model achieves a better performance, highlighting its application potential in seabed sediment detection and mapping.

Probabilistic neural network based seabed sediment recognition method for side-scan sonar imagery

Recognition of seabed sediment is one of the critical foundations of marine exploitation. This paper proposes a probabilistic neural network (PNN) based method to improve the identification accuracy of seabed sediment from side-scan sonar imagery. The feature set of side-scan images consists of two types of features, namely textural features and color features. In this study, partial eigenvalues of the gray co-occurrence matrix are selected as the textural feature, and the color features are represented by color moments. Combining textural features with color features, we get the input matrix, which is then fed into PNN for classification. PNN calculates the distance between the sample eigenvector to be predicted and the training sample eigenvector, then accumulates the probability belonging to a certain category. Finally, PNN outputs the forecasted class of samples eigenvector with the largest posterior probability. It is the first time that PNN has been used in seabed sediment classification from side-scan sonar imagery. Compared with the traditional clustering methods, PNN improved the accuracy of the classification and attained a highest accuracy of 92.2%.