Evidence map›Paper›PMID 33816941›Full record

ArticlePeerJ. Computer science2020

Hydrographic data inspection and disaster monitoring using shipborne radar small range images with electronic navigation chart.

Jin Xu, Baozhu Jia, Xinxiang Pan, Ronghui Li, Liang Cao, Can Cui, Haixia Wang, Bo Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jin Xu *Maritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Baozhu Jia *Maritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Xinxiang PanMaritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Ronghui LiMaritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Liang CaoMaritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Can CuiCivil Aviation College, Shenyang Aerospace University, Shenyang, Liaoning, China.
Haixia WangNavigation College, Dalian Martime University, Dalian, Liaoning, China.
Bo LiMaritime College, Guangdong Ocean University, Zhanjiang, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Shipborne radars cannot only enable navigation and collision avoidance but also play an important role in the fields of hydrographic data inspection and disaster monitoring. In this paper, target extraction methods for oil films, ships and coastlines from original shipborne radar images are proposed. First, the shipborne radar video images are acquired by a signal acquisition card. Second, based on remote sensing image processing technology, the radar images are preprocessed, and the contours of the targets are extracted. Then, the targets identified in the radar images are integrated into an electronic navigation chart (ENC) by a geographic information system. The experiments show that the proposed target segmentation methods of shipborne radar images are effective. Using the geometric feature information of the targets identified in the shipborne radar images, information matching between radar images and ENC can be realized for hydrographic data inspection and disaster monitoring.

Indexed as

Electronic navigation chartInformation fusionOil spillShipborne radar

Identifiers

PMID33816941
PMCPMC7924651

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.