scholarly journals Ship Classification Based on Attention Mechanism and Multi-Scale Convolutional Neural Network for Visible and Infrared Images

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2022
Author(s):  
Yongmei Ren ◽  
Jie Yang ◽  
Zhiqiang Guo ◽  
Qingnian Zhang ◽  
Hui Cao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Infrared Images ◽  
Visible Image ◽  
Multi Scale ◽  
Ship Classification ◽  
Fusion Features

Visible image quality is very susceptible to changes in illumination, and there are limitations in ship classification using images acquired by a single sensor. This study proposes a ship classification method based on an attention mechanism and multi-scale convolutional neural network (MSCNN) for visible and infrared images. First, the features of visible and infrared images are extracted by a two-stream symmetric multi-scale convolutional neural network module, and then concatenated to make full use of the complementary features present in multi-modal images. After that, the attention mechanism is applied to the concatenated fusion features to emphasize local details areas in the feature map, aiming to further improve feature representation capability of the model. Lastly, attention weights and the original concatenated fusion features are added element by element and fed into fully connected layers and Softmax output layer for final classification output. Effectiveness of the proposed method is verified on a visible and infrared spectra (VAIS) dataset, which shows 93.81% accuracy in classification results. Compared with other state-of-the-art methods, the proposed method could extract features more effectively and has better overall classification performance.

scholarly journals Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network

2019 ◽  
Vol 11 (14) ◽  
pp. 1678 ◽  
Author(s):  
Yongyong Fu ◽  
Ziran Ye ◽  
Jinsong Deng ◽  
Xinyu Zheng ◽  
Yibo Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Contextual Information ◽  
Feature Space ◽  
Classification Performance ◽  
Support Vector ◽  
Marine Resources ◽  
Marine Aquaculture ◽  
Multi Scale ◽  
Coastal Marine

Marine aquaculture plays an important role in seafood supplement, economic development, and coastal ecosystem service provision. The precise delineation of marine aquaculture areas from high spatial resolution (HSR) imagery is vital for the sustainable development and management of coastal marine resources. However, various sizes and detailed structures of marine objects make it difficult for accurate mapping from HSR images by using conventional methods. Therefore, this study attempts to extract marine aquaculture areas by using an automatic labeling method based on the convolutional neural network (CNN), i.e., an end-to-end hierarchical cascade network (HCNet). Specifically, for marine objects of various sizes, we propose to improve the classification performance by utilizing multi-scale contextual information. Technically, based on the output of a CNN encoder, we employ atrous convolutions to capture multi-scale contextual information and aggregate them in a hierarchical cascade way. Meanwhile, for marine objects with detailed structures, we propose to refine the detailed information gradually by using a series of long-span connections with fine resolution features from the shallow layers. In addition, to decrease the semantic gaps between features in different levels, we propose to refine the feature space (i.e., channel and spatial dimensions) using an attention-based module. Experimental results show that our proposed HCNet can effectively identify and distinguish different kinds of marine aquaculture, with 98% of overall accuracy. It also achieves better classification performance compared with object-based support vector machine and state-of-the-art CNN-based methods, such as FCN-32s, U-Net, and DeeplabV2. Our developed method lays a solid foundation for the intelligent monitoring and management of coastal marine resources.

scholarly journals Track Prediction for HF Radar Vessels Submerged in Strong Clutter Based on MSCNN Fusion with GRU-AM and AR Model

2021 ◽  
Vol 13 (11) ◽  
pp. 2164
Author(s):  
Ling Zhang ◽  
Jingzhi Zhang ◽  
Jiong Niu ◽  
Q. M. Jonathan Wu ◽  
Gangsheng Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Attention Mechanism ◽  
Hf Radar ◽  
Ar Model ◽  
Trajectory Prediction ◽  
Long Distance ◽  
Multi Scale ◽  
Wave Radar ◽  
Wide Range

High-frequency (HF) surface-wave radar has a wide range of applications in marine monitoring due to its long-distance, wide-area, and all-weather detection ability. However, the accurate detection of HF radar vessels is severely restricted by strong clutter and interference, causing the echo of vessels completely submerged by clutter. As a result, the target cannot be detected and tracked for a period of time under the influence of strong clutter, which causes broken trajectories. To solve this problem, we propose an HF radar-vessel trajectory-prediction method based on a multi-scale convolutional neural network (MSCNN) that combines a gated recurrent unit and attention mechanism (GRU-AM) and a fusion with an autoregressive (AR) model. The vessel’s latitude and longitude information obtained by the HF radar is sent into the convolutional neural network (CNN) with different window lengths in parallel, and feature fusion is performed on the extracted multi-scale features. The deep GRU model is built to learn the time series with the GRU structure to preserve historical information. Different weights are given to the features using the temporal attention mechanism (AM), which helps the network learn the key information. The linear information on latitude and longitude at the current timestep is forecast by combining the AR model with the trajectory output from the AM to achieve a combination of linear and nonlinear prediction models. To make full use of the HF radar tracking information, the broken trajectory prediction is carried out by forward and backward computation using data from before and after the fracture, respectively. Weights are then assigned to the two predicted results by the entropy-value method to obtain the final ship trajectory by weighted summation. Field experiments show that the proposed method can accurately forecast the trajectories of vessels concealed in clutter. In comparison with other mainstream methods, the new method performs better in estimation accuracy for HF radar vessels concealed in clutter.

scholarly journals Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images

2019 ◽  
Vol 9 (20) ◽  
pp. 4209 ◽  
Author(s):  
Yongmei Ren ◽  
Jie Yang ◽  
Qingnian Zhang ◽  
Zhiqiang Guo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Structural Information ◽  
Confusion Matrix ◽  
Weather Conditions ◽  
Local Binary Patterns ◽  
Feature Representation ◽  
Ship Classification ◽  
High Level

The appearance of ships is easily affected by external factors—illumination, weather conditions, and sea state—that make ship classification a challenging task. To facilitate realization of enhanced ship-classification performance, this study proposes a ship classification method based on multi-feature fusion with a convolutional neural network (CNN). First, an improved CNN characterized by shallow layers and few parameters is proposed to learn high-level features and capture structural information. Second, handcrafted features of the histogram of oriented gradients (HOG) and local binary patterns (LBP) are combined with high-level features extracted by the improved CNN in the last fully connected layer to obtain discriminative feature representation. The handcrafted features supplement the edge information and spatial texture information of the ship images. Then, the Softmax function is used to classify different types of ships in the output layer. Effectiveness of the proposed method is evaluated based on its application to two datasets—one self-built and the other publicly available, called visible and infrared spectrums (VAIS). As observed, the proposed method demonstrated attainment of average classification accuracies equal to 97.50% and 93.60%, respectively, when applied to these datasets. Additionally, results obtained in terms of the F1-score and confusion matrix demonstrate the proposed method to be superior to some state-of-the-art methods.

scholarly journals Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3937 ◽  
Author(s):  
Tengda Huang ◽  
Sheng Fu ◽  
Haonan Feng ◽  
Jiafeng Kuang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Time Domain ◽  
Attention Mechanism ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale

Recently, deep learning technology was successfully applied to mechanical fault diagnosis. The convolutional neural network (CNN), as a prevalent deep learning model, occupies a place in intelligent fault diagnosis, which reduces the need for human feature extraction and prior knowledge, thereby achieving an end-to-end intelligent fault diagnosis model. However, the data for mechanical fault diagnosis in practical application are limited, the CNN model is too deep and too complex, making it prone to overfitting, and a model with too simple a structure and shallow layers cannot fully learn the effective features of the data. Convolutional filters with fixed window sizes are widely used in existing CNN models, which cannot flexibly select variable pivotal features. The model may be interfered with by redundant information in feature maps during training. Therefore, in this paper, a novel shallow multi-scale convolutional neural network with attention is proposed for bearing fault diagnosis. The shallow multi-scale convolutional neural network structure can fully learn the feature information of input data without overfitting. For the first time, a feature attention mechanism is developed for fault diagnosis to adaptively select features for classification more effectively, where the pivotal feature was emphasized, and the redundant feature was weakened through an attention mechanism. The time frequency representations as the input of the model were obtained from the vibration time domain signals, which contain the complete time domain and frequency domain information of the vibration signals. Compared with the current popular diagnostic methods, the results show that the proposed diagnostic method has fairly high accuracy, and its performance is superior to the existing methods. The average recognition accuracy was 99.86%, and the weak recognition rate of I-07 and I-14 labels was improved.

scholarly journals Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network

2019 ◽  
Vol 11 (4) ◽  
pp. 419 ◽  
Author(s):  
Qiaoqiao Shi ◽  
Wei Li ◽  
Ran Tao ◽  
Xu Sun ◽  
Lianru Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fractional Fourier Transform ◽  
Gabor Filter ◽  
Feature Learning ◽  
Feature Representation ◽  
Optical Remote Sensing ◽  
Discriminative Feature ◽  
Ship Classification ◽  
High Level

As an important part of maritime traffic, ships play an important role in military and civilian applications. However, ships’ appearances are susceptible to some factors such as lighting, occlusion, and sea state, making ship classification more challenging. This is of great importance when exploring global and detailed information for ship classification in optical remote sensing images. In this paper, a novel method to obtain discriminative feature representation of a ship image is proposed. The proposed classification framework consists of a multifeature ensemble based on convolutional neural network (ME-CNN). Specifically, two-dimensional discrete fractional Fourier transform (2D-DFrFT) is employed to extract multi-order amplitude and phase information, which contains such important information as profiles, edges, and corners; completed local binary pattern (CLBP) is used to obtain local information about ship images; Gabor filter is used to gain the global information about ship images. Then, deep convolutional neural network (CNN) is applied to extract more abstract features based on the above information. CNN, extracting high-level features automatically, has performed well for object classification tasks. After high-feature learning, as the one of fusion strategies, decision-level fusion is investigated for the final classification result. The average accuracy of the proposed approach is 98.75% on the BCCT200-resize data, 92.50% on the original BCCT200 data, and 87.33% on the challenging VAIS data, which validates the effectiveness of the proposed method when compared to the existing state-of-art algorithms.

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