A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images

Abstract

Ship detection and classification systems from satellite images are challenging tasks with their requirements of feature extracting, advanced pre-processing, a variety of parameters obtained from satellites and other types of images, and analyzing of images. The dissimilarity of results, enhanced dataset requirement, the intricacy of the problem domain, general use of Synthetic Aperture Radar (SAR) images and problems on generalizability are some topics of the issues related to ship detection. In this study, we propose a Deep Convolutional Neural Network (DCNN) model for detecting the ships using the satellite images as inputs. Our model has acquired an adequate accuracy value by just using a pre-processed satellite image with a deep learning model built from scratch. The designed CNN model is constructed with a plain and easy to implement form in particular to the preferred satellite image set. Visual and graphical results show that the proposed model provides an efficient detection process with an accuracy of 99.60%.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

“…Artificial intelligence with deep convolutional neural networks has made significant breakthroughs in many fields, which will be widely used in the aerospace field, such as situational awareness [1], intelligent obstacle avoidance [2], and remote sensing image in-orbit detection [3]. The biggest challenge for applying artificial intelligence in the aerospace field is that these artificial intelligence algorithms based on deep convolutional neural networks require a lot of memory and computational cost.…”