Airbus ship detection from satellite imagery using frequency domain learning

Al-Saad, Mina; Aburaed, Nour; Panthakkan, Alavikunhu; Mansoori, Saeed Al; Al-Ahmad, Hussain; Marshall, Stephen

doi:10.1117/12.2600168

Cited by 9 publications

(9 citation statements)

References 21 publications

(18 reference statements)

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“…The classical CNN-based target-detection algorithm has achieved good results on natural image data sets, but in remote sensing images the background is often complex and the scale of the ship target changes greatly, so the classical target-detection algorithm sometimes cannot effectively extract the ship features. Al-saad et al [20] proposed a method of frequency domain enhancement by embedding a wavelet transform into Faster R-CNN, and before extracting ROI the original image was decomposed into high and low frequency components for training and testing in the frequency domain, thus improving the detection accuracy. This method is simple and easy, but the accuracy is not high.…”

Section: Related Workmentioning

confidence: 99%

Double Augmentation: A Modal Transforming Method for Ship Detection in Remote Sensing Imagery

Mou,

Fan,

Jiang

et al. 2024

Remote Sensing

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Ship detection in remote sensing images plays an important role in maritime surveillance. Recently, convolution neural network (CNN)-based methods have achieved state-of-the-art performance in ship detection. Even so, there are still two problems that remain in remote sensing. One is that the different modal images observed by multiple satellite sensors and the existing dataset cannot satisfy network-training requirements. The other is the false alarms in detection, as the ship target is usually faint in real view remote sensing images and many false-alarm targets can be detected in ocean backgrounds. To solve these issues, we propose a double augmentation framework for ship detection in cross-modal remote sensing imagery. Our method can be divided into two main steps: the front augmentation in the training process and the back augmentation verification in the detection process; the front augmentation uses a modal recognition network to reduce the modal difference in training and in using the detection network. The back augmentation verification uses batch augmentation and results clustering to reduce the rate of false-alarm detections and improve detection accuracy. Real-satellite-sensing experiments have been conducted to demonstrate the effectiveness of our method, which shows promising performance in quantitative evaluation metrics.

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Section: Related Workmentioning

confidence: 99%

Double Augmentation: A Modal Transforming Method for Ship Detection in Remote Sensing Imagery

Mou,

Fan,

Jiang

et al. 2024

Remote Sensing

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“…For example, in HRSC2016 [59], there are only two or three targets in an image, most of which are large-scale targets. The scenes of NWPU VHR-10 [60] and the Airbus ship dataset [61] are more singular with the coastal background. Subsequently, we have proposed the VRS ship dataset [54] (VRS-SD) in our previous study, which contains various maritime disturbances, such as thin clouds, islands, sea waves, and wake waves.…”

Section: Datasetmentioning

confidence: 99%

LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

Tian,

Wang,

Zhu

et al. 2023

Remote Sensing

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Ship detection technology has achieved significant progress recently. However, for practical applications, lightweight ship detection still remains a very challenging problem since small ships have small relative scales in wide images and are easily missed in the background. To promote the research and application of small-ship detection, we propose a new remote sensing image dataset (VRS-SD v2) and provide a fog simulation method that reflects the actual background in remote sensing ship detection. The experiment results show that the proposed fog simulation is beneficial in improving the robustness of the model for extreme weather. Further, we propose a lightweight detector (LMSD-Net) for ship detection. Ablation experiments indicate the improved ELA-C3 module can efficiently extract features and improve the detection accuracy, and the proposed WGC-PANet can reduce the model parameters and computation complexity to ensure a lightweight nature. In addition, we add a Contextual Transformer (CoT) block to improve the localization accuracy and propose an improved localization loss specialized for tiny-ship prediction. Finally, the overall performance experiments demonstrate that LMSD-Net is competitive in lightweight ship detection among the SOTA models. The overall performance achieves 81.3% in AP@50 and could meet the lightweight and real-time detection requirements.

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“…As far as we know, there are many public datasets for VRS ship detection. Based on the background of marine ship detection, we mainly introduce MWPU VHR-10 [54], HRSC2016 [55], MASATI [56], and Airbus Ship dataset [57], which have important influences in the field. The detailed differences between them and the VRS ship dataset are summarized in Table 1.…”

Section: Vrs Ship Datasetmentioning

confidence: 99%

Ship Detection in Visible Remote Sensing Image Based on Saliency Extraction and Modified Channel Features

et al. 2022

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Ship detection in visible remote sensing (VRS) images has been widely used in the military and civil fields. However, the various backgrounds and the variable scale and orientation bring great difficulties to effective detection. In this paper, we propose a novel ship target detection scheme based on small training samples. The scheme contains two main stages: candidate region extraction and ship identification. In the first stage, we propose a visual saliency detection model based on the difference in covariance statistical characteristics to quickly locate potential ships. Moreover, the multi-scale fusion for the saliency model is designed to overcome the problem of scale variation. In the second stage, we propose a three-channel aggregate feature, which combines a rotation-invariant histogram of oriented gradient and the circular frequency feature. The feature can identify the ship target well by avoiding the impact of its rotation and shift. Finally, we propose the VRS ship dataset that contains more realistic scenes. The results on the VRS ship dataset demonstrate that the saliency model achieves the best AUC value with 0.9476, and the overall detection achieves a better performance of 65.37% in terms of AP@0.5:0.95, which basically meets the need of the detection tasks.

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Airbus ship detection from satellite imagery using frequency domain learning

Cited by 9 publications

References 21 publications

Double Augmentation: A Modal Transforming Method for Ship Detection in Remote Sensing Imagery

Double Augmentation: A Modal Transforming Method for Ship Detection in Remote Sensing Imagery

LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

Ship Detection in Visible Remote Sensing Image Based on Saliency Extraction and Modified Channel Features

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