scholarly journals Airbus ship detection from satellite imagery using frequency domain learning

2021 ◽  
Author(s):  
Mina Al-Saad ◽  
Nour Aburaed ◽  
Alavikunhu Panthakkan ◽  
Saeed Al Mansoori ◽  
Hussain Al-Ahmad ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Satellite Imagery ◽  
Ship Detection ◽  
Domain Learning

Fast and Accurate Lane Detection via Frequency Domain Learning

2021 ◽  
Author(s):  
Yulin He ◽  
Wei Chen ◽  
Zhengfa Liang ◽  
Dan Chen ◽  
Yusong Tan ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Lane Detection ◽  
Domain Learning

scholarly journals New Approaches and Tools for Ship Detection in Optical Satellite Imagery

2020 ◽  
Vol 1642 ◽  
pp. 012003
Author(s):  
Aaron Walter Avila Cordova ◽  
William Condori Quispe ◽  
Remy Jorge Cuba Inca ◽  
Wilder Nina Choquehuayta ◽  
Eveling Castro Gutierrez
Keyword(s):  
Satellite Imagery ◽  
Ship Detection ◽  
New Approaches

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

2020 ◽  
Vol 68 (2) ◽  
pp. 17-21
Author(s):  
Kodanda Dhar Naik ◽  
Manisha Rautaray ◽  
Shivam Sharma ◽  
Sourav Mohapatra ◽  
Subhashree Dash ◽  
...  
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Sequential Algorithm ◽  
Ship Detection

Small Ship Detection on Optical Satellite Imagery with YOLO and YOLT

2020 ◽  
pp. 664-677 ◽  
Author(s):  
Wilder Nina ◽  
William Condori ◽  
Vicente Machaca ◽  
Juan Villegas ◽  
Eveling Castro
Keyword(s):  
Satellite Imagery ◽  
Ship Detection ◽  
Small Ship

Fully automated procedure for ship detection using optical satellite imagery

2008 ◽  
Author(s):  
C. Corbane ◽  
E. Pecoul ◽  
L. Demagistri ◽  
M. Petit
Keyword(s):  
Satellite Imagery ◽  
Ship Detection

scholarly journals Visual Saliency via Multiscale Analysis in Frequency Domain and Its Applications to Ship Detection in Optical Satellite Images

2022 ◽  
Vol 15 ◽  
Author(s):  
Ying Yu ◽  
Jun Qian ◽  
Qinglong Wu
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Multiscale Analysis ◽  
Satellite Images ◽  
Visual Saliency ◽  
Bottom Up ◽  
Ship Detection ◽  
Saliency Maps ◽  
Proposed Model ◽  
Optical Satellite Images

This article proposes a bottom-up visual saliency model that uses the wavelet transform to conduct multiscale analysis and computation in the frequency domain. First, we compute the multiscale magnitude spectra by performing a wavelet transform to decompose the magnitude spectrum of the discrete cosine coefficients of an input image. Next, we obtain multiple saliency maps of different spatial scales through an inverse transformation from the frequency domain to the spatial domain, which utilizes the discrete cosine magnitude spectra after multiscale wavelet decomposition. Then, we employ an evaluation function to automatically select the two best multiscale saliency maps. A final saliency map is generated via an adaptive integration of the two selected multiscale saliency maps. The proposed model is fast, efficient, and can simultaneously detect salient regions or objects of different sizes. It outperforms state-of-the-art bottom-up saliency approaches in the experiments of psychophysical consistency, eye fixation prediction, and saliency detection for natural images. In addition, the proposed model is applied to automatic ship detection in optical satellite images. Ship detection tests on satellite data of visual optical spectrum not only demonstrate our saliency model's effectiveness in detecting small and large salient targets but also verify its robustness against various sea background disturbances.

scholarly journals A complete processing chain for ship detection using optical satellite imagery

2010 ◽  
Vol 31 (22) ◽  
pp. 5837-5854 ◽  
Author(s):  
Christina Corbane ◽  
Laurent Najman ◽  
Emilien Pecoul ◽  
Laurent Demagistri ◽  
Michel Petit
Keyword(s):  
Satellite Imagery ◽  
Ship Detection ◽  
Processing Chain ◽  
Complete Processing
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