Authors

Y Mohan Das

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S Arifa Thabasum

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S Rizwana

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

D Tabussum

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S Zaheer Ahmad

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

N Nithin

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S Shabeena

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

This project describes the concept to detect ships from sea images taken from satellites and these images are called as ‘Synthetic aperture radar (SAR)’. Ships can be detected from SAR images using Post CNN Algorithm. It will be trained with ship images from VGG ImageNet Network, while training it extract features from images using its height, width and image colour channel. CNN filter train images features map from multiple layers of convolution neural network. All object detection from image will be maintained in train vector with value 1 and other background features marked as 0. Whenever new SAR test image uploaded then proposed algorithm will apply train vector on SAR test image to detect objects with ships features.

Keywords

Convolutional Neural Network Image classification and Recognition CNN SAR

Citation of this Article

Y Mohan Das, S Arifa Thabasum, S Rizwana, D Tabussum, S Zaheer Ahmad, N Nithin, & S Shabeena. (2025). Shipnet Post CNN Ship Extraction from High Resolution Optical Images. Journal of Artificial Intelligence and Emerging Technologies. 2(4), 1-4. Article DOI: https://doi.org/10.47001/JAIET/2025.204001

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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