A novel procedure for land masking in ocean-land segmentation from SAR images

Gu, Dandan; Xu, Xiaojian

doi:10.1109/icspcc.2014.6986249

Cited by 3 publications

(1 citation statement)

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“…The accuracy of the sea-land segmentation can be improved by evaluating the radiometric intensity gradient in zone closed to the sea in SAR image [ 8 ]. Experimental results from real SAR images demonstrate the usefulness of methods of wavelet transform [ 9 , 10 ] and threshold techniques [ 11 , 12 ]. Compared with earlier methods, such approaches do not require geography prior knowledge, but are necessary for complex mathematical theory.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network

Pan

You

2018

Sensors

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Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.

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