A Maximum-Information-Minimum-Redundancy-Based Feature Fusion Framework for Ship Classification in Moderate-Resolution SAR Image

Zhou, Gaoyu; Zhang, Gong; Xue, Biao

doi:10.3390/s21020519

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Selain itu, kapal dapat dideteksi menggunakan radar, terutama dalam cuaca buruk atau saat jarak pandang buruk. Synthetic aperture radar (SAR), yang berfungsi secara terus menerus dan tidak terpengaruh oleh kondisi cuaca yang beragam, telah diimplementasikan secara luas dalam pemantauan kapal dan maritim (Zhou et al, 2021). Selain itu, pemantauan maritim telah menunjukkan harapan untuk penerapan radar pasif GNSS yang menggunakan sinyal GNSS sebagai penerang (Pastina et al, 2021).…”

Section: Pendahuluanunclassified

Optimalisasi Penggunaan Radio Detection and Ranging (Radar) Untuk Keselamatan Dalam Bernavigasi Pada Alur Pelayaran Sempit Di KMP Virgo 18

Malatunduh,

Widarbowo,

Kendek

et al. 2024

JPB

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Penelitian ini bertujuan untuk mengoptimalkan penggunaan Radio Detection and Ranging (RADAR) dalam meningkatkan keselamatan navigasi di alur pelayaran sempit serta mengatasi kendala terkait. Faktor seperti kegagalan fungsi RADAR dalam mendeteksi benda di atas permukaan air menunjukkan pentingnya perawatan dan optimalisasi teknologi ini. Kendala lain termasuk kurangnya kesadaran dan keterampilan manusia dalam manuver kapal, serta faktor eksternal seperti kondisi cuaca buruk dan masuknya kapal ke alur pelayaran sempit. Penelitian menggunakan metodologi kualitatif dengan investigasi dilakukan di KMP Virgo 18 antara Oktober 2021 hingga September 2022. Teknik akuisisi data termasuk tinjauan literatur, wawancara, dan observasi. Hasil menyoroti strategi untuk meningkatkan keterampilan pengguna RADAR melalui pelatihan reguler, pemeliharaan rutin RADAR, dan kesiapsiagaan dalam menghadapi situasi darurat. Implikasi penelitian ini adalah pentingnya pemahaman mendalam terhadap kendala RADAR dan keterampilan manusia dalam navigasi kapal. Rekomendasi strategis dapat membantu industri maritim mengembangkan kebijakan yang lebih aman. Dengan demikian, peningkatan keselamatan berlayar dapat dicapai melalui optimalisasi teknologi RADAR dan pemahaman yang lebih baik terhadap tantangan navigasi di alur pelayaran sempit.

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Section: Pendahuluanunclassified

Optimalisasi Penggunaan Radio Detection and Ranging (Radar) Untuk Keselamatan Dalam Bernavigasi Pada Alur Pelayaran Sempit Di KMP Virgo 18

Malatunduh,

Widarbowo,

Kendek

et al. 2024

JPB

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“…Each method has its outstanding aspects. Zhou et al proposed a projection curve model matching-based method for packaging bottle detection, using projection completeness, the weighted sum of offset expectation, and variance of matching points relative to the model as similarity measures, and using packaging bottle model matching to complete packaging bottle detection [12]. Teng et al proposed a packaging bottle detection algorithm based on a priori shape information and an active contour model, using color and edge information to remove shadows and extract packaging bottle contour; introducing a priori knowledge of packaging bottle shape, establishing an a priori shape model of packaging bottle with the implicit representation of level set symbols, and constructing an active contour energy construction function with this constraint, using the variational method to find its minimum value, using shape alignment and level set method e segmentation curve of the packaging bottle is evolved to obtain the contour of the packaging bottle and then complete the detection [13].…”

Section: Current Status Of Researchmentioning

confidence: 99%

A Study on the Design and Implementation of an Improved AdaBoost Optimization Mathematical Algorithm Based on Recognition of Packaging Bottles

Guo-zhu

Tsai

2022

Mathematical Problems in Engineering

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In this paper, a special design system is developed based on the design of the packaging bottle to achieve the effective acquisition of the image of the cross-section of the packaging bottle to be measured under the condition of limited space size, avoiding the distortion of the object to be measured. At the same time, the image of the area where the target packaging bottle is located is segmented, and the curve features are quickly determined and effectively matched with the template library to realize the recognition of the shape features of the bottle. In this paper, the design of the packaging bottle is first designed by mechanism design and 3D modeling, followed by rapid prototyping methods such as 3D printing, and the prototype is made for functional verification. Finally, the transmission speed and stability of the design system for packaging bottle recognition are improved through structural analysis and optimization methods. To realize the intelligent control of the packaging bottle transmission and identification system, the hardware control circuit is designed and the relevant intelligent control program is prepared based on the embedded system so that the packaging bottles in the transmission process can be quickly and accurately positioned and identified. An improved AdaBoost algorithm is proposed for packaging bottle detection. In the process of algorithm learning, the Haar features are too large and time-consuming, and the training sample is cropped to remove the sample edge pixels, which effectively reduces the number of features, thus reducing the computation. The proposed optical flow method is used to obtain the motion region in the video image as the region of interest, and the canny operator is used in the region of interest for edge detection, and the region of interest is filtered by the edge energy to exclude the noninterest region. Finally, the AdaBoost algorithm is used to detect the region of interest, which reduces the detection area and decreases the detection time. The improved AdaBoost algorithm has a high accuracy improvement over the traditional AdaBoost algorithm for the recognition of various packaging bottles with relatively suitable training set samples, and the system recognition time has reached the requirements of industrial recognition.

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“…In the early days, classification of ships from SAR images was explored using polarimetric data [1] and it required an intensive expertise in this domain. With the development and advancement of machine learning (ML) techniques, several studies have 2 developed and tested ML algorithms with handcrafted features such as scattering features [2][3][4], superstructure scattering features [5,6], geometric features [7,8], histogram of oriented gradient (HOG) [9], fusion features [10,11], single-pol COSMO-SkyMed images using the statistical and structural feature vector [12], Although these ML techniques were capable of classifying ships automatically from the SAR images, their performance is limited by the chosen handcrafted features.…”

Section: Introductionmentioning

confidence: 99%

Attention-Guided Convolution Neural Network Assisted With Handcrafted Features for Ship Classification in Low-Resolution Sentinel-1 SAR Image Data

Bhattacharjee,

Shanmugam,

Das

2024

IEEE Access

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Automatic maritime target classification is essential for effective coastal surveillance and maritime applications. Synthetic aperture radar (SAR) data have been increasingly used for ship classification due to the recent wider open-source data accessibility and the development of deep learning techniques. The convolution neural network (CNN) is a popular deep learning technique used for ship classification using high-resolution labeled SAR image data, despite the limited accessibility of these data due to the substantial cost. The labeled low-resolution SAR image data are less expensive, but their utility is limited due to high false alarms and poor classification accuracy caused by the difficulties in recognizing and discriminating the discrete features for specific classes. To overcome these constraints, we propose an attention-guided CNN model assisted with handcrafted features for ship classification in low-resolution SAR images. This model was trained with OpenSARShip data and tested on Sentinel-1 20 m images with Automatic Identification System (AIS) data class labels from five ports across the world. For the four types of ships (tanker, bulk carrier, container ships and fishing vessels), the experimental results showed an overall classification accuracy of 87.5%, a Kappa score of 0.75, a precision of 86%, and a F1 score of 86.3%. The intercomparison analysis on test data showed statistically a higher performance for the proposed model than for the baseline VGG16 model, which indicates the effectives of the proposed model for various maritime applications.

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A Maximum-Information-Minimum-Redundancy-Based Feature Fusion Framework for Ship Classification in Moderate-Resolution SAR Image

Cited by 7 publications

References 37 publications

Optimalisasi Penggunaan Radio Detection and Ranging (Radar) Untuk Keselamatan Dalam Bernavigasi Pada Alur Pelayaran Sempit Di KMP Virgo 18

Optimalisasi Penggunaan Radio Detection and Ranging (Radar) Untuk Keselamatan Dalam Bernavigasi Pada Alur Pelayaran Sempit Di KMP Virgo 18

A Study on the Design and Implementation of an Improved AdaBoost Optimization Mathematical Algorithm Based on Recognition of Packaging Bottles

Attention-Guided Convolution Neural Network Assisted With Handcrafted Features for Ship Classification in Low-Resolution Sentinel-1 SAR Image Data

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