Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory

Islam, Naveed; Zerr, Benoît

doi:10.1016/j.oceaneng.2017.06.061

Cited by 7 publications

(6 citation statements)

References 29 publications

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“…The weights are updated by minimizing the loss function to obtain the optimal weight parameters of the network. The optimal weight parameters are obtained from the mean square error loss function as shown in equation (2).…”

Section: Loss Function and Its Improvementmentioning

confidence: 99%

“…Due to the low resolution and severe speckle noise of sonar images, it is difficult to achieve accurate segmentation by ordinary image segmentation methods [1]. At present, there are many sonar image segmentation methods, such as clustering method [2], edge detection method [3], Markov Random Filed method [4] and combining with specific mathematical theory methods [5,6]. However, these methods have poor anti-speckle noise capabilities and cannot achieve automatic segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Research on Convolutional Neural Network Model for Sonar IMAGE Segmentation

Jiao

Zhao

2018

MATEC Web Conf.

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The speckle noise of sonar images affects the human interpretation and automatic recognition of images seriously. It is important and difficult to realize the precision segmentation of sonar image with speckle noise in the field of image processing. Full convolution neural network (FCN) has the advantage of accepting arbitrary size image and preserving spatial information of original input image. In this paper, the image features are obtained by autonomic learning of convolutional neural network, the original learning rules based on the mean square error loss function is improved. Taking the pixel as the processing unit, the segmentation method based on FCN model with relative loss function(FCN-RLF) for small submarine sonar image is proposed, sonar image pixel-level segmentation is achievied. Experimental results show that the improved algorithm can improve the segmentation accuracy and keep the edge and detail of sonar image better. The proposed model has better ability to reject sonar image speckle noise.

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Section: Loss Function and Its Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Convolutional Neural Network Model for Sonar IMAGE Segmentation

Jiao

Zhao

2018

MATEC Web Conf.

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“…Classification methods (e.g., support vector machine (SVM) [21][22][23][24][25][26][27], neural networks [28][29][30]) are supervised learning techniques that involve manually annotated examples in a training step. Supervised learning highly depends on the quality of the training set (number of training examples, labeling samples [27,28,[31][32][33][34][35][36]), besides a tedious task of annotating data by a human. On the other hand, clustering approaches do not need training to ultimately label samples, by techniques such as K-means clustering [23,31,[37][38][39][40], Fuzzy C-means [41][42][43][44][45], and other types of clustering [21,33,46].…”

Section: Introductionmentioning

confidence: 99%

“…Supervised learning highly depends on the quality of the training set (number of training examples, labeling samples [27,28,[31][32][33][34][35][36]), besides a tedious task of annotating data by a human. On the other hand, clustering approaches do not need training to ultimately label samples, by techniques such as K-means clustering [23,31,[37][38][39][40], Fuzzy C-means [41][42][43][44][45], and other types of clustering [21,33,46]. However, clustering schemes are important in terms of computation requirements and similarity measurements.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

Yousefi

Ibarra-Castanedo

Chamberland³

et al. 2021

Remote Sensing

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Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

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“…These characteristics greatly influence feature extraction and image recognition. Researchers used various methods to pre-process the SSS images and studied traditional segmentation algorithms [7,8,9,10] to process them. While it achieves certain results, the process is time consuming and the accuracy remains relatively low [11].…”

Section: Introductionmentioning

confidence: 99%

RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images

Wang

et al. 2019

Sensors

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Real-time processing of high-resolution sonar images is of great significance for the autonomy and intelligence of autonomous underwater vehicle (AUV) in complex marine environments. In this paper, we propose a real-time semantic segmentation network termed RT-Seg for Side-Scan Sonar (SSS) images. The proposed architecture is based on a novel encoder-decoder structure, in which the encoder blocks utilized Depth-Wise Separable Convolution and a 2-way branch for improving performance, and a corresponding decoder network is implemented to restore the details of the targets, followed by a pixel-wise classification layer. Moreover, we use patch-wise strategy for splitting the high-resolution image into local patches and applying them to network training. The well-trained model is used for testing high-resolution SSS images produced by sonar sensor in an onboard Graphic Processing Unit (GPU). The experimental results show that RT-Seg can greatly reduce the number of parameters and floating point operations compared to other networks. It runs at 25.67 frames per second on an NVIDIA Jetson AGX Xavier on 500*500 inputs with excellent segmentation result. Further insights on the speed and accuracy trade-off are discussed in this paper.

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Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory

Cited by 7 publications

References 29 publications

Research on Convolutional Neural Network Model for Sonar IMAGE Segmentation

Research on Convolutional Neural Network Model for Sonar IMAGE Segmentation

Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images

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