A Simple Ship Echo Identification Procedure With SeaSonde HF Radar

Chuang, Laurence Zsu Hsin; Chung, Yu-Jen; Tang, Shiue–Cheng

doi:10.1109/lgrs.2015.2487363

Cited by 18 publications

(8 citation statements)

References 8 publications

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“…Researchers have employed techniques based on synthetic aperture radar (SAR) to recognise ship types. Chuang et al proposed a ship echo recognition framework by applying a novel adaptive detection technique to the original coastal radar images (Chuang et al, 2015). Makedonas et al proposed a two-stage hierarchical feature selection algorithm to discriminate three civilian ship types (i.e., cargo carrier, fisher and tanker) (Makedonas et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Ship Type Recognition via a Coarse-to-Fine Cascaded Convolution Neural Network

et al. 2020

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Most previous research has handled the task of ship type recognition by exploring hand-craft ship features, which may fail to distinguish ships with similar visual appearances. This situation motivates us to propose a novel deep learning based ship type recognition framework which we have named coarse-to-fine cascaded convolution neural network (CFCCNN). First, the proposed CFCCNN framework formats the input training ship images and data, and provides trainable input data for the hidden layers of the CFCCNN. Second, the coarse and fine steps are run in a nesting manner to explore discriminative features for different ship types. More specifically, the coarse step is trained in a similar manner to the traditional convolution neural network, while the fine step introduces regularisation mechanisms to extract more intrinsic ship features, and fine tunes parameter settings to obtain better recognition performance. Finally, we evaluate the performance of the CFCCNN model for recognising the most common types of merchant ship (oil tanker, container, LNG tanker, chemical carrier, general cargo, bulk carrier, etc.). The experimental results show that the proposed framework obtains better recognition performance than the conventional methods of ship type recognition.

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Section: Introductionmentioning

confidence: 99%

Ship Type Recognition via a Coarse-to-Fine Cascaded Convolution Neural Network

et al. 2020

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“…Turley et al [330] applied multiple CFAR techniques to HF radar data to acquire a high probability of detection for a given probability of false alarm and obtain the tracking stage of radar processing with constant and low false alarm rate. Other related techniques have also been applied to the radar signals to detect vessels, such as the adaptive detection technique [331], morphological component analysis [332], curvilinear regression analysis [333], and range-Doppler (RD) spectrum enhancement [334]. For instance, Khan and Power [323] investigated low altitude aircraft detection using an HF radar system.…”

Section: ) Target Detectionmentioning

confidence: 99%

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

Amani¹,

Mohseni

Layegh

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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As discussed in the previous part of this review paper, Remote Sensing (RS) creates unprecedented opportunities by providing a variety of systems with different characteristics to study and monitor oceans. Part 1 of this review paper was dedicated to reviewing passive RS systems and their main applications in the ocean. Here, in part 2, seven active RS systems, including scatterometers, altimeters, gravimeters, Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR), Sound Navigation and Ranging (SONAR), High-Frequency (HF) radars are comprehensively reviewed. For consistency, this part is structured similarly to part 1. The aforementioned systems, along with their characteristics and primary applications, are introduced in separate sections. This review paper provides useful information to all students and researchers who are interested in the oceanographic applications of active RS systems.

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“…While some of the imagebased systems operate on conventional visual features such as colour, contour, and shape, other systems use deep learning (DL) for feature extraction. In [2], the authors introduced an STR system on coastal radar images. In another study [3], researchers proposed a dual-stage feature selection method to conduct recognition among three types (cargo, fisher, and tanker).…”

Section: Introductionmentioning

confidence: 99%

Ship Type Recognition using Deep Learning with FFT Spectrums of Audio Signals

Yıldırım

2023

ECJSE

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Ship type recognition has gained serious interest in applications required in the maritime sector. A large amount of the studies in literature focused on the use of images taken by shore cameras, radar images, and audio features. In the case of image-based recognition, a very large number and variety of ship images must be collected. In the case of audio-based recognition, systems may suffer from the background noise. In this study, we present a method, which uses the frequency domain characteristics with an image-based deep learning network. The method computes the fast Fourier transform of sound records of ships and generates the frequency vs magnitude graphs as images. Next, the images are given into the ResNet50 network for classification. A public dataset with nine different ship types is used to test the performance of the proposed method. According to the results, we obtained a 99% accuracy rate.

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A Simple Ship Echo Identification Procedure With SeaSonde HF Radar

Cited by 18 publications

References 8 publications

Ship Type Recognition via a Coarse-to-Fine Cascaded Convolution Neural Network

Ship Type Recognition via a Coarse-to-Fine Cascaded Convolution Neural Network

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

Ship Type Recognition using Deep Learning with FFT Spectrums of Audio Signals

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