A novel nearest feature learning classifier for ship target detection in optical remote sensing images

Bo Huang; Tingfa Xu; Yuxin Luo; Sining Chen; Bo Liu; Bo Yuan

doi:10.1007/978-981-10-6571-2_73

A novel nearest feature learning classifier for ship target detection in optical remote sensing images

Bo Huang, Tingfa Xu^*, Yuxin Luo, Sining Chen, Bo Liu, Bo Yuan

^*Corresponding author for this work

School of Optics and Photonics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Satellite remote sensing data is becoming more and more abundant, In order to realize automatic detection of ships on the sea surface, this paper presents an adaptive intelligent ship detection method, a novel nearest feature learning classifier (NFLC), which combines the scale invariant feature transform (SIFT) feature extraction with nearest feature learning classification. Due to the wide variety of detection ships, the NFLC can obtain a better experimental result than conventional detection methods. The detection accuracy is enhanced by the feature training in large databases and the performance of the system can be continuously improved through the target learning. In addition, compared to convolutional neural network algorithm, it can save the computation time by using the nearest feature matching. The result shows that almost all of the offshore ships can be detected, and the total detection rate is 89.3% with 1000 experimental optical remote sensing images from Google Earth data.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems
Editors	Qilian Liang, Min Jia, Jiasong Mu, Wei Wang, Xuhong Feng, Baoju Zhang
Publisher	Springer Verlag
Pages	600-606
Number of pages	7
ISBN (Print)	9789811065705
DOIs	https://doi.org/10.1007/978-981-10-6571-2_73
Publication status	Published - 2019
Event	6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 - Harbin, China Duration: 14 Jul 2017 → 16 Jul 2017

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	463
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017
Country/Territory	China
City	Harbin
Period	14/07/17 → 16/07/17

Keywords

Nearest feature learning
Optical remote sensing images
Ship detection
The NFLC

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10.1007/978-981-10-6571-2_73

Cite this

Huang, B., Xu, T., Luo, Y., Chen, S., Liu, B., & Yuan, B. (2019). A novel nearest feature learning classifier for ship target detection in optical remote sensing images. In Q. Liang, M. Jia, J. Mu, W. Wang, X. Feng, & B. Zhang (Eds.), Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems (pp. 600-606). (Lecture Notes in Electrical Engineering; Vol. 463). Springer Verlag. https://doi.org/10.1007/978-981-10-6571-2_73

Huang, Bo ; Xu, Tingfa ; Luo, Yuxin et al. / A novel nearest feature learning classifier for ship target detection in optical remote sensing images. Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. editor / Qilian Liang ; Min Jia ; Jiasong Mu ; Wei Wang ; Xuhong Feng ; Baoju Zhang. Springer Verlag, 2019. pp. 600-606 (Lecture Notes in Electrical Engineering).

@inproceedings{d556d36ef0b14904b2185bfe221f7690,

title = "A novel nearest feature learning classifier for ship target detection in optical remote sensing images",

abstract = "Satellite remote sensing data is becoming more and more abundant, In order to realize automatic detection of ships on the sea surface, this paper presents an adaptive intelligent ship detection method, a novel nearest feature learning classifier (NFLC), which combines the scale invariant feature transform (SIFT) feature extraction with nearest feature learning classification. Due to the wide variety of detection ships, the NFLC can obtain a better experimental result than conventional detection methods. The detection accuracy is enhanced by the feature training in large databases and the performance of the system can be continuously improved through the target learning. In addition, compared to convolutional neural network algorithm, it can save the computation time by using the nearest feature matching. The result shows that almost all of the offshore ships can be detected, and the total detection rate is 89.3% with 1000 experimental optical remote sensing images from Google Earth data.",

keywords = "Nearest feature learning, Optical remote sensing images, Ship detection, The NFLC",

author = "Bo Huang and Tingfa Xu and Yuxin Luo and Sining Chen and Bo Liu and Bo Yuan",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 ; Conference date: 14-07-2017 Through 16-07-2017",

year = "2019",

doi = "10.1007/978-981-10-6571-2_73",

language = "English",

isbn = "9789811065705",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "600--606",

editor = "Qilian Liang and Min Jia and Jiasong Mu and Wei Wang and Xuhong Feng and Baoju Zhang",

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address = "Germany",

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Huang, B, Xu, T, Luo, Y, Chen, S, Liu, B & Yuan, B 2019, A novel nearest feature learning classifier for ship target detection in optical remote sensing images. in Q Liang, M Jia, J Mu, W Wang, X Feng & B Zhang (eds), Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol. 463, Springer Verlag, pp. 600-606, 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017, Harbin, China, 14/07/17. https://doi.org/10.1007/978-981-10-6571-2_73

A novel nearest feature learning classifier for ship target detection in optical remote sensing images. / Huang, Bo; Xu, Tingfa; Luo, Yuxin et al.
Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. ed. / Qilian Liang; Min Jia; Jiasong Mu; Wei Wang; Xuhong Feng; Baoju Zhang. Springer Verlag, 2019. p. 600-606 (Lecture Notes in Electrical Engineering; Vol. 463).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A novel nearest feature learning classifier for ship target detection in optical remote sensing images

AU - Huang, Bo

AU - Xu, Tingfa

AU - Luo, Yuxin

AU - Chen, Sining

AU - Liu, Bo

AU - Yuan, Bo

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2019.

PY - 2019

Y1 - 2019

N2 - Satellite remote sensing data is becoming more and more abundant, In order to realize automatic detection of ships on the sea surface, this paper presents an adaptive intelligent ship detection method, a novel nearest feature learning classifier (NFLC), which combines the scale invariant feature transform (SIFT) feature extraction with nearest feature learning classification. Due to the wide variety of detection ships, the NFLC can obtain a better experimental result than conventional detection methods. The detection accuracy is enhanced by the feature training in large databases and the performance of the system can be continuously improved through the target learning. In addition, compared to convolutional neural network algorithm, it can save the computation time by using the nearest feature matching. The result shows that almost all of the offshore ships can be detected, and the total detection rate is 89.3% with 1000 experimental optical remote sensing images from Google Earth data.

AB - Satellite remote sensing data is becoming more and more abundant, In order to realize automatic detection of ships on the sea surface, this paper presents an adaptive intelligent ship detection method, a novel nearest feature learning classifier (NFLC), which combines the scale invariant feature transform (SIFT) feature extraction with nearest feature learning classification. Due to the wide variety of detection ships, the NFLC can obtain a better experimental result than conventional detection methods. The detection accuracy is enhanced by the feature training in large databases and the performance of the system can be continuously improved through the target learning. In addition, compared to convolutional neural network algorithm, it can save the computation time by using the nearest feature matching. The result shows that almost all of the offshore ships can be detected, and the total detection rate is 89.3% with 1000 experimental optical remote sensing images from Google Earth data.

KW - Nearest feature learning

KW - Optical remote sensing images

KW - Ship detection

KW - The NFLC

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U2 - 10.1007/978-981-10-6571-2_73

DO - 10.1007/978-981-10-6571-2_73

M3 - Conference contribution

AN - SCOPUS:85048683052

SN - 9789811065705

T3 - Lecture Notes in Electrical Engineering

SP - 600

EP - 606

BT - Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems

A2 - Liang, Qilian

A2 - Jia, Min

A2 - Mu, Jiasong

A2 - Wang, Wei

A2 - Feng, Xuhong

A2 - Zhang, Baoju

PB - Springer Verlag

T2 - 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017

Y2 - 14 July 2017 through 16 July 2017

ER -

Huang B, Xu T, Luo Y, Chen S, Liu B, Yuan B. A novel nearest feature learning classifier for ship target detection in optical remote sensing images. In Liang Q, Jia M, Mu J, Wang W, Feng X, Zhang B, editors, Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. Springer Verlag. 2019. p. 600-606. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-10-6571-2_73

A novel nearest feature learning classifier for ship target detection in optical remote sensing images

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