Ship detection in SAR imagery: A comparison study

Iervolino, Pasquale; Guida, Raffaella; Lumsdon, Parivash; Janoth, J.; Clift, Melanie; Minchella, A.; Bianco, Paolo

doi:10.1109/igarss.2017.8127384

Cited by 23 publications

(11 citation statements)

References 6 publications

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“…Prominently assumed distributions in literature are Gaussian, K-Distribution [13], Weibull [14]. The popular ship detectors as per the literature and the frequently exploited data for this research domain are mentioned in [16], [17], [18] ALOS [19], [20], [17], [21] SENTINEL-1 [22], [15], [23], [24] RADARSAT-2 [22], [25], [26], [27], [20], [28], [29], [17] CosmoSkymed [22], [25], [30], [28] UAVSAR [26], [3] TerraSAR-X [31], [30], [29], [32], [24] RADARSAT [19], [22], [15], [30], [28], 15, [32], [21] GP-PNF [19], [31], [20] SPAN [16], [17] DoP [26] m…”

Section: A Organization Of the Papermentioning

confidence: 99%

Empirical Analysis of Ship Detection and Dimension Estimation from RISAT-1 Compact Polarimetric Data

Shah,

Zaveri,

Sharma

et al. 2020

IJEAT

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The detection of ship and estimation of its characteristics has its significance in tracking these sea vessels for surveillance. It also has its importance as an ancillary information for marine applications like oil spill detection and its source tracking. This paper is an aspiration towards exploring the strength of RISAT-1 data retrieved in circular transmit linear receive mode. We propose a method to select the detected ship and get the dimension estimation of the segmented ship for further decision making. The proposed method has been implemented in python and tested for various inputs. The verification of the same is done as a de-facto measure based on location or by supporting measurements obtained through Google Maps.

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Section: A Organization Of the Papermentioning

confidence: 99%

Empirical Analysis of Ship Detection and Dimension Estimation from RISAT-1 Compact Polarimetric Data

Shah,

Zaveri,

Sharma

et al. 2020

IJEAT

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“…T PFA is the threshold given a fixed probability of false alarm, respectively. As for the CFAR algorithms, the GLRT can be applied by using a moving window and computing the clutter and target parameters along with the threshold at each iteration (Adaptive Threshold algorithm); or, viceversa, the target and clutter parameters can be estimated through the MLE for a single representative window yielding to a fix threshold (Global Threshold algorithm) [10].…”

Section: Generalized Likelihood Ratio Testmentioning

confidence: 99%

SAR Ship Detection for Rough Sea Conditions

Iervolino

Guida

Amitrano

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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In the Synthetic Aperture Radar (SAR) framework many detection algorithms and techniques have been published in the recent literature; however the detection of vessels whose dimensions are in the order of the image spatial resolution is still challenging in rough sea state scenarios. This issue is addressed in the paper presented here by comparing rationale and performance of two detectors developed by the same authors: the Generalized Likelihood Ratio Test (GLRT) and the Intensity Dual-Polarization Ratio Anomaly Detector (iDPolRAD). Both detectors are tested on a dualpolarization VV/VH Interferometric Wide Swath Sentinel-1 image acquired over the Suruga Bay on the Pacific Coast of Japan. The theory is presented here and the two detectors are compared against the Cell Average-Constant False Alarm Algorithm (CA-CFAR) showing both better performance than CFAR in terms of false alarms rejection.

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“…Furthermore, SAR offers high-resolution that can detect ships with different sizes covering wide areas on the shores and oceans. Developing an automated SAR ship detection system [4] is crucial for maritime traffic surveillance and fundamental for the national and international security and economy [5][6] [7].…”

Section: Introductionmentioning

confidence: 99%

SemiSegSAR: A Semi-Supervised Segmentation Algorithm for Ship SAR Images

Giraldo

Al-Saad

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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Automatic ship segmentation from high-resolution Synthetic Aperture Radar (SAR) remote sensing images has been a topic of interest that has gradually gained attention over the years due to the abundance of earth observation sensors. Recently, deep learning methods have provided a breakthrough increasing the performance greatly by using large amount of labeled data. Yet, the high cost related to the samples labeling and their scarcity result in significant limitation of their wide use. Therefore, it is crucial to overcome the unlabeled inputs challenge and develop semi-supervised learning approaches to enhance the machine learning models capacity. Our letter proposes a semi-supervised segmentation algorithm for SAR images named SemiSegSAR based on the use of Graph Signal Processing. This method includes instance segmentation; texture and statistical SAR features to represent the nodes of the graph; K-nearest neighbors to construct the graph; and Sobolev minimization algorithm to tackle the problem of semi-supervised semantic segmentation. The proposed algorithm is trained and tested using the publicly available SSDD and HRSID ship detection datasets. Experiments show that SemiSegSAR outperforms the current state-of-the-art semi-supervised and supervised methods while requiring only few labeled data.

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Ship detection in SAR imagery: A comparison study

Cited by 23 publications

References 6 publications

Empirical Analysis of Ship Detection and Dimension Estimation from RISAT-1 Compact Polarimetric Data

Empirical Analysis of Ship Detection and Dimension Estimation from RISAT-1 Compact Polarimetric Data

SAR Ship Detection for Rough Sea Conditions

SemiSegSAR: A Semi-Supervised Segmentation Algorithm for Ship SAR Images

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