A New Visual Attention Model Designed for SAR

Object detection in synthetic aperture radar (SAR) images, which is a fundamental but 10 challenging problem in the field of satellite image interpretation, plays an important role for a wide 11 range of applications and is receiving significant attention in recent years. Recently, the ability of 12 human visual system to detect targets with visual saliency is extraordinarily fast and reliable. 13 However, visual computational modeling of SAR image scene still remains a challenge. This paper 14 designs a visual attention model for SAR images. Firstly, we propose a novel approach for 15 computing the local texture coarseness of the input image, then our model constructs the 16 corresponding feature maps. Next a new mechanism of feature fusion is adopted to replace the 17 linear additive mechanism of classical models to obtain the final saliency map. Moreover, the gray 18 values of focus of attention (FOA) in all feature maps are taken into account, our model chooses the 19 best saliency representation, the filter and threshold segmentation of saliency maps can be used to 20 extract the salient regions accurately through the multi-scale competition strategy, thereby 21 completing this operation for visual saliency detection in SAR images. Finally, the paper gives the 22 framework based on classical ITTI model. In the paper, several types of satellite data, such as 23 TerraSAR-X (TS-X) and Radarsat-2, are used to evaluate the performance of visual models. The 24 results show that our model provides superior performance than classical models. By further 25 contrasting with classical visual models, our model reduce the false alarm caused by speckle noise, 26 its detection speed is greatly improved, and it is increased by 25% to 45%. 27