Remote Sensing Salient Map Fusion Method Based on Gradient Optimization

摘要：

　　Target detection,change detection,and region of interest extraction are important research areas in remote sensing image processing.In order to reduce computational redundancy and improve image processing efficiency and accuracy,visual saliency models are widely used in the preprocessing stage of these fields.In this paper,a novel of remote sensing salient map fusion method based on gradient optimization is proposed.The local and global salient maps are obtained by wavelet transform and spectral residual method.The gradient salient map is solved by the maximum gradient optimization,and the fused salient map is reconstructed by Haar wavelet.The experimental results show that the fused salient map can combine the effective information of local and global saliency maps,and the detection accuracy is better than the global saliency map or the local saliency map,which has a better effect than the salient map fused by the simple method.

关键词： Remote sensing image Saliency detection Image fusion Gradient optimization

作者: Zhou HUANG Huai-xin CHEN Yun-zhi YANG Dao-cai Fu Chang-yin WANG

作者单位: University of Electronic Science and Technology of China,Chengdu,China CETC Special Mission Aircraft System Engineering Co.Ltd,Chengdu,China

会议类型: 国际会议

会议名称: 2019 International Conference on Informatics, Control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

会议地点: 上海

会议语种:英文

页码: 123-131

在线出版日期: 2019-06-16（万方平台首次上网日期，不代表论文的发表时间）