Ship Size Extraction for Sentinel-1 Images Based on Dual-Polarization Fusion and Nonlinear Regression: Push Error Under One Pixel

Li, Boying; Liu, Bin; Guo, Weiwei; Zhang, Zenghui; Wang, Yu

doi:10.1109/tgrs.2018.2841882

Cited by 11 publications

(17 citation statements)

References 33 publications

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“…For the lower spatial resolution SAR image in Case Study A, an empirical correction has been applied to account for the observed overestimation in ship width returned by the SUMO algorithm with improvements shown (see Figure 14c). However, to improve the estimates of SAR-derived ship dimensions in Sentinel-1 (IW mode) imagery, other more sophisticated SAR ship size estimation techniques are needed (e.g., [63,67]).…”

Section: Effectiveness Of Ship Classificationmentioning

confidence: 99%

Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

Rodger

Guida

2020

Remote Sensing

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A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety and security. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly difficult in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data. Two case studies in the UK are used to evaluate the performance of the classification-aided data association technique based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic, and the confidence in the data association is improved when using class (i.e., ship type) information.

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Section: Effectiveness Of Ship Classificationmentioning

confidence: 99%

Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

Rodger

Guida

2020

Remote Sensing

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“…It drastically increases the computation time as seen from the elements of the kernel matrix and from the mean and variance of the modelled distribution but once the response surface is constructed, RCS can be accurately predicted, hence making this approach suitable for integration in multi‐disciplinary modelling and simulation environments. Several machine learning approaches have previously been applied to address various problems such as target classification [20], image de‐noising [21], detection of unmanned aerial vehicles [22], ship size extraction [23] and significant wave height estimation [24]. However, this method in particular, can be utilised in the applications where RCS optimisation with respect to geometric parameters is required such as stealth aircrafts, ships and satellite design.…”

Section: Introductionmentioning

confidence: 99%

Multi‐frequency analysis of Gaussian process modelling for aperiodic RCS responses of a parameterised aircraft model

Bilal

Hamza

Taj

et al. 2020

IET Radar, Sonar & Navigation

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“…With the advent of a new generation of satellites and fast-growing image analysis technology, it is feasible to extract more detailed ship information from SAR images in addition to its detection [7]. The ship's length and width provide essential information for ship classification and marine surveillance [8]. In most cases, it is difficult to identify the ship's type directly from the SAR image, and the size information can provide useful help.…”

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confidence: 99%

“…The geometric feature of the ship's NRCS, such as the length and width of the minimum bounding rectangle (MBR), provides an initial size for estimating a ship's ground size. Meanwhile, the ship's superstructure, the environment, the sea-ship interaction, and the imaging system can influence the NRCS from the ship [8]. These factors lead to a huge gap between the initial size and the ground size.…”

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confidence: 99%

“…For more information, see https://creativecommons.org/licenses/by/4.0/ 30 m (relative error: 16%), and the MAE of width is 11 m (relative error: 37%). To further reduce the estimation error, researchers took the initial ship size of the second step as the initial information and performed the third step by statistical/machine learning (ML) methods, such as multiple linear regression [13], the kernel-based method [7], and nonlinear regression [8]. Among them, the nonlinear regression model is a typical representation.…”

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confidence: 99%

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A Deep Learning Model to Extract Ship Size From Sentinel-1 SAR Images

Ren

Xu³

2022

IEEE Trans. Geosci. Remote Sensing

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This study develops a deep learning (DL) model to extract the ship size from Sentinel-1 synthetic aperture radar (SAR) images, named SSENet. We employ a single shot multibox detector (SSD)-based model to generate a rotatable bounding box (RBB) for the ship. We design a deepneural-network (DNN)-based regression model to estimate the accurate ship size. The hybrid inputs to the DNN-based model include the initial ship size and orientation angle obtained from the RBB and the abstracted features extracted from the input SAR image. We design a custom loss function named mean scaled square error (MSSE) to optimize the DNN-based model. The DNN-based model is concatenated with the SSD-based model to form the integrated SSENet. We employ a subset of the Open-SARShip, a data set dedicated to Sentinel-1 ship interpretation, to train and test SSENet. The training/testing data set includes 1500/390 ship samples. Experiments show that SSENet is capable of extracting the ship size from SAR images end to end. The mean absolute errors (MAEs) are under 0.8 pixels, and their length and width are 7.88 and 2.23 m, respectively. The hybrid input significantly improves the model performance. The MSSE reduces the MAE of length by nearly 1 m and increases the MAE of width by 0.03m compared to the mean square error (MSE) loss function. Compared with the well-performed gradient boosting regression (GBR) model, SSENet reduces the MAE of length by nearly 2 m (18.68%) and that of width by 0.06 m (2.51%). SSENet shows robustness on different training/testing sets.

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Ship Size Extraction for Sentinel-1 Images Based on Dual-Polarization Fusion and Nonlinear Regression: Push Error Under One Pixel

Cited by 11 publications

References 33 publications

Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

Multi‐frequency analysis of Gaussian process modelling for aperiodic RCS responses of a parameterised aircraft model

A Deep Learning Model to Extract Ship Size From Sentinel-1 SAR Images

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