Ship Detection from Satellite Imagery Using Deep Learning Techniques to Control Deep Sea Oil Spills

被引：0

作者：

Jamal, Mohamed Fuad Amin Mohamed ^{[1
]}

Almeer, Shaima Shawqi ^{[2
]}

Pulari, Sini Raj ^{[1
]}

机构：

[1] Bahrain Polytech, Dept ICT, EDICT, Isa Town, Bahrain

[2] Natl Space Sci Agcy, Space Data Anal Lab, Al Hidd, Bahrain

来源：

INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING AND COMMUNICATIONS, ICICC 2022, VOL 1 | 2023年 / 473卷

关键词：

CNN; Oil spills; Sea pollution; Deep learning; Satellite imagery;

D O I：

10.1007/978-981-19-2821-5_32

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Our planet Earth is presently being disturbed by a variety of environmental concerns. One of the top critical environmental issues affecting our planet's ecosystem is oil spills. Oil spills mostly occur due to ship leakage which highly influences our food supply chain and leads to a high-level drop in the economic division. Therefore, monitoring and tracking those vessels are extremely vital to determine the responsible ships for the occurrence of an incident. This study revolves around an implementation of an automated ship detection software application by building a high-level algorithm that embeds deep learning networks. The algorithm is built in a way that can predict and classify vessels from high-resolution satellite images with 98.5% accuracy.

引用

页码：365 / 375

页数：11

共 18 条

[11] ImageNet Classification with Deep Convolutional Neural Networks [J].

Krizhevsky, Alex ;

Sutskever, Ilya ;

Hinton, Geoffrey E. .

COMMUNICATIONS OF THE ACM, 2017, 60 (06) :84-90

[12]

Kumar R, 2021, INT C INNOVATIVE COM

[13]

Madhavan S, 2017, DEEP LEARNING ARCHIT

[14]

RawatW WangZ, 2017, NEURALCOMPUT

[15]

Society LO, 2011, TANK TECHN LIM DOUBL

[16] A deep learning approach to ship detection using satellite imagery [J].

Stofa, Marzuraikah Mohd ;

Zulkifley, Mohd Asyraf ;

Zaki, Siti Zulaikha Muhammad .

10TH IGRSM INTERNATIONAL CONFERENCE AND EXHIBITION ON GEOSPATIAL & REMOTE SENSING, 2020, 540

[17]

TensorFlow, 2021, TENSORFLOW

[18]

Thoma M., 2017, Analysis and optimization of convolutional neural network architectures

← 1 2 →