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Fine-Grained ship recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

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Computers, Materials & Continua 2024年第6期79卷 5243-5271页

作者： Shiwen Song Rui Zhang Min Hu Feiyao Huang Department of Aerospace Science and Technology Space Engineering UniversityBeijing101416China

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national ***,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising ***-grained recognition of multi-modality images imposes higher requirements on the dataset *** key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion *** attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s *** this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained recognition of ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing *** provides two types of annotation format files,as well as segmentation mask images corresponding to the ship ***,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been *** the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive *** Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on ***-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,*** experiments demons

关键词： Multi-modality dataset ship recognition fine-grained recognition attention mechanism

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An Automated Approach to Passive Sonar Classification Using Binary Image Features

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Journal of Marine Science and Application 2015年第3期14卷 327-333页

作者： Vahid Vahidpour Amlr Rastegarnia Azam Khalili Department of Electrical Engineering Malayer University Malayer 65719-95863 1ran

This paper proposes a new method for ship recognition and classification using sound produced and radiated underwater. To do so, a three-step procedure is proposed. First, the preprocessing operations are utilized to reduce noise effects and provide signal for feature extraction. Second, a binary image, made from frequency spectrum of signal segmentation, is formed to extract effective features. Third, a neural classifier is designed to classify the signals. Two approaches, the proposed method and the fractal-based method are compared and tested on real data. The comparative results indicated better recognition ability and more robust performance of the proposed method than the fractal-based method. Therefore, the proposed method could improve the recognition accuracy of underwater acoustic targets.

关键词： binary image passive sonar neural classifier ship recognition short-time Fourier transform fractal-based method

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