2008
DOI: 10.3390/s8052959
|View full text |Cite
|
Sign up to set email alerts
|

Using SPOT-5 HRG Data in Panchromatic Mode for Operational Detection of Small Ships in Tropical Area

Abstract: Nowadays, there is a growing interest in applications of space remote sensing systems for maritime surveillance which includes among others traffic surveillance, maritime security, illegal fisheries survey, oil discharge and sea pollution monitoring. Within the framework of several French and European projects, an algorithm for automatic ship detection from SPOT–5 HRG data was developed to complement existing fishery control measures, in particular the Vessel Monitoring System. The algorithm focused on feature… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

3
34
0
1

Year Published

2012
2012
2019
2019

Publication Types

Select...
4
3
1

Relationship

0
8

Authors

Journals

citations
Cited by 63 publications
(38 citation statements)
references
References 7 publications
3
34
0
1
Order By: Relevance
“…Despite this, often the cloud cover is only partial and is then useful to classify clouds. They come in all shapes and are generally white with redness within 0.45 < NR < 0.6 and therefore difficult to discriminate from boats [10,11] and ice floes. The S2 image in Figure 10 was selected because it contains a large number of smaller clouds, which challenge the classification algorithm.…”
Section: Cloudsmentioning
confidence: 99%
See 2 more Smart Citations
“…Despite this, often the cloud cover is only partial and is then useful to classify clouds. They come in all shapes and are generally white with redness within 0.45 < NR < 0.6 and therefore difficult to discriminate from boats [10,11] and ice floes. The S2 image in Figure 10 was selected because it contains a large number of smaller clouds, which challenge the classification algorithm.…”
Section: Cloudsmentioning
confidence: 99%
“…By visual inspection, one can distinguish ships from clouds from more complex texture features. We have thus far only included the spectral and spatial classifiers as described above, but can foresee further improvements of our physical classification algorithm embodying a number of more complex object texture features as for example described in [10] or as widely employed in large area SAR imagery [2].…”
Section: Cloudsmentioning
confidence: 99%
See 1 more Smart Citation
“…Before training, the coefficients are scaled to [0, 1]d, and the learning rate is set as 0.1. The number of training batches depends on the size of data set, usually between [10,100]. Different training batches should contain different classes of training samples to achieve better performance.…”
Section: Ship Feature Representation and Classificationmentioning
confidence: 99%
“…When the S2-B is launched, the revisit time for most of Europe will be two or three days. Unless it is obscured by clouds, the multispectral and SAR satellite imagery will provide frequent coverage of the Earth that increases with latitude, and this condition is particularly useful for arctic surveillance [2,3], ship detection [4][5][6][7][8][9], oil spills [10], crops and trees [11], as well as hyperspectral imaging [12].…”
Section: Introductionmentioning
confidence: 99%