International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 68 | Issue 2 | Year 2020 | Article Id. IJCTT-V68I2P103 | DOI : https://doi.org/10.14445/22312803/IJCTT-V68I2P103

Ship Detection from Satellite Imagery In Deep Learning: Using Sequential Algorithm

Kodanda Dhar Naik, Manisha Rautaray, Shivam Sharma, Sourav Mohapatra, Subhashree Dash , Abhishek Parida

Received Revised Accepted
02 Jan 2020 11 Feb 2020 14 Feb 2020

Citation :

Kodanda Dhar Naik, Manisha Rautaray, Shivam Sharma, Sourav Mohapatra, Subhashree Dash , Abhishek Parida, "Ship Detection from Satellite Imagery In Deep Learning: Using Sequential Algorithm," International Journal of Computer Trends and Technology (IJCTT), vol. 68, no. 2, pp. 17-21, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I2P103

Abstract

Ship detection is an inherent process supporting tasks such as fishery management, ship search, marine traffic monitoring and control, and helps in the prevention of illegal activities. So far, sea and shore monitoring has been carried out by ship patrols and aircrafts along with sea vessel detection from data from space-borne platforms. While investigating state of the art methods used for ship detection from different platforms using optical images, we found a significant problem with occurrence of a ship wake. This phenomena may prohibit correct detection of ship location and results in overestimating the ship size as the ship and its wake are often considered as being part of the same object in image or wakes are distinguished as a separate ship due to their possible similar brightness compared with sea vessel. In order to reduce the impact of ship wakes we investigated the behaviour of images in different colour spaces to provide data with little or almost no trace of ship wake. Object of interest were detected through the use of image segmentation. Applied method uses edge detection based on the gradient magnitude calculation.

Keywords

Deep Learning, Remote Sensing Convolutional Neural Network, Keras Model, Sequential Algorithm

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