A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Amir Hussain¹  Qingxu Xiong²  Zhenyu Yue²  Fei Gao²  Jun Wang²  Huiyu Zhou³ 

[1] Cognitive Big Data and Cyber-Informatics (CogBID) Laboratory the School of Computing, Edinburgh Napier University, Edinburgh, U.K.;School of Electronic and Information Engineering, Beihang University, Beijing, China;School of Informatics, University of Leicester, Leicester, U.K.;

关键词: Attention mechanism;    conditional random field (CRF);    fully convolutional network (FCN);    image segmentation;    synthetic aperture radar (SAR);   

DOI  :  10.1109/JSTARS.2020.3016064

来源: DOAJ

【 摘 要 】

As an important step of synthetic aperture radar image interpretation, synthetic aperture radar image segmentation aims at segmenting an image into different regions in terms of homogeneity. Because of the deficiency of the labeled samples and the existence of speckling noise, synthetic aperture radar image segmentation is a challenging task. We present a new method for synthetic aperture radar image segmentation in this article. Due to the large size of the original synthetic aperture radar image, we first divide the input image into small slices. Then the image slices are input to the attention-based fully convolutional network for obtaining the segmentation results. Finally, the fully connected conditional random field is adopted for improving the segmentation performance of the network. The innovations of our method are as follows: 1) The attention-based fully convolutional network is embedded with the multiscale attention network which is capable of enhancing the extraction of the image features through three strategies, namely, multiscale feature extraction, channel attention extraction, and spatial attention extraction. 2) We design a new loss function for the attention fully convolutional network by combining Lovasz-Softmax and cross-entropy losses. The new loss allows us to simultaneously optimize the intersection over union and the pixel classification accuracy of the segmentation results. The experiments are performed on two airborne synthetic aperture radar image databases. It has been proved that our method is superior to other state-of- the-art image segmentation approaches.

【 授权许可】

Unknown