Ship velocity estimation in SAR images using multitask deep learning

Heiselberg, Peder; Sørensen, Kristian A.; Heiselberg, H.

doi:10.1016/j.rse.2023.113492

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…It clearly supported that the elimination of range velocity and acceleration from the derivation of ( 11) was valid. It demonstrated that vessel velocity estimation is possible from the proposed phase refocusing function without using wake signatures to measure azimuth offset [45]- [47].…”

Section: Discussionmentioning

confidence: 99%

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

Song,

Kim,

Hwang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Along with vessel detection, vessel recognition in high-resolution SAR images was necessary in order to monitor marine vessels effectively. However, lack of target data and phase defocusing of target from its velocity limited the recognition performance, especially when using detectors based on artificial intelligence. This study accordingly proposed effective vessel recognition in high-resolution ICEYE spotlight SAR images consecutively utilizing (i) vessel detector robust to defocused moving vessels and (ii) mitigation of moving target phase distortion. In order to apply quantitative and qualitative training data enhancement, a target velocity SAR phase refocusing function was developed. The proposed target velocity SAR phase refocusing function generated defocused SLC image with respect to different target azimuth velocity, which can be utilized for both training data augmentation and refocusing of velocity-induced phase distortion. Achievement of stable vessel recognition performance was enabled from (i) robust vessel detection on defocused moving vessels and (ii) well-focused detected vessel targets, both of which were consecutively applied using the proposed target velocity SAR phase refocusing function. Vessel detection results demonstrated robust performance regardless of vessel motion and vessel recognition results significantly improved after phase refocusing, both of which were subject to quantitative and qualitative training data enhancement. Performance of the proposed algorithm was analyzed both in terms of phase focusing and velocity estimation. Refocusing performance outperformed that of conventional state-of-the-art autofocusing algorithm, modified Phase Gradient Autofocusing, while azimuth velocity estimation derived the average offset of 0.68 m/s, which was regarded more accurate than previous azimuth velocity estimators based on single-channel SAR image.

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Section: Discussionmentioning

confidence: 99%

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

Song,

Kim,

Hwang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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“…Each ship was on average found a couple of times, yielding 16.000 AIS annotated ship images 1 . For a more detailed description of the detection and annotation process, we refer to [10], [11]. Six examples of annotated ship images are presented in Figure 1.…”

Section: Datamentioning

confidence: 99%

Identification of Ships in Satellite Images

Heiselberg,

Pedersen,

Sørensen

et al. 2024

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

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Satellite imagery has become a fundamental part for maritime monitoring and safety. Correctly estimating a ship's identity is a vital tool. We present a method based on facial recognition for identifying ships in satellite images. A large ship dataset is constructed from Sentinel-2 multispectral images and annotated by matching to the Automatic Identification System. Our dataset contains 7.000 unique ships, for which a total of 16.000 images are acquired. The method uses a convolutional neural network to extract a feature vector from the ship images and embed it on a hypersphere. Distances between ships can then be calculated via the embedding vectors. The network is trained using a triplet loss function, such that minimum distances are achieved for identical ships and maximum distances to different ships. Comparing a ship image to a reference set of ship images yields a set of distances. Ranking the distances provides a list of the most similar ships. The method correctly identifies a ship on average 60% of the time as the first in the list. Larger ships are easier to identify than small ships, where the image resolution is a limitation.

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“…Acquiring suitable datasets that contain high-resolution optical and SAR data to support such tasks is also a challenging issue. Recent studies have started to investigate the use of SAR or optical remote sensing data for multi-task learning [33][34][35], demonstrating the potential of multitask learning in remote sensing. However, numerous challenges remain, such as integrating multi-source data and developing effective algorithms for joint learning.…”

Section: Multi-modal Fusion and Joint Learning For Remote Sensingmentioning

confidence: 99%

“…Existing studies primarily concentrate on analyzing remote sensing mechanisms or utilizing multi-view remote sensing images for relative height estimation through dense matching [17,31,32]. Recent endeavors have explored the utilization of SAR or optical remote sensing data for multi-task learning [7,[33][34][35]. Additionally, some studies have integrated ground object height and RGB images to perform semantic segmentation tasks [36].…”

Section: Introductionmentioning

confidence: 99%

MFTSC: A Semantically Constrained Method for Urban Building Height Estimation Using Multiple Source Images

Chen,

Yan,

Huang

2023

Remote Sensing

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The use of remote sensing imagery has significantly enhanced the efficiency of building extraction; however, the precise estimation of building height remains a formidable challenge. In light of ongoing advancements in computer vision, numerous techniques leveraging convolutional neural networks and Transformers have been applied to remote sensing imagery, yielding promising outcomes. Nevertheless, most existing approaches directly estimate height without considering the intrinsic relationship between semantic building segmentation and building height estimation. In this study, we present a unified architectural framework that integrates the tasks of building semantic segmentation and building height estimation. We introduce a Transformer model that systematically merges multi-level features with semantic constraints and leverages shallow spatial detail feature cues in the encoder. Our approach excels in both height estimation and semantic segmentation tasks. Specifically, the coefficient of determination (R2) in the height estimation task attains a remarkable 0.9671, with a root mean square error (RMSE) of 1.1733 m. The mean intersection over union (mIoU) for building semantic segmentation reaches 0.7855. These findings underscore the efficacy of multi-task learning by integrating semantic segmentation with height estimation, thereby enhancing the precision of height estimation.

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Ship velocity estimation in SAR images using multitask deep learning

Cited by 11 publications

References 23 publications

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing

Identification of Ships in Satellite Images

MFTSC: A Semantically Constrained Method for Urban Building Height Estimation Using Multiple Source Images

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