Change Detection for High Resolution Remote Sensing Image Based on Co-saliency Strategy
Change Detection for High Resolution Remote Sensing Image Based on Co-saliency Strategy
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DOI：
10.1109/multi-temp.2019.8866911
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Qingle Guo;Junping Zhang
中科院分区：
文献类型：
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作者：
Qingle Guo;Junping Zhang
遥感图像的变化检测具有广泛的应用前景。本文从基于目标的方法出发，提出了一种基于共显著性策略的多时相高分辨率图像变化检测算法。首先生成差分特征和对数差分特征融合后的最终差分图像，通过Gabor小波变换得到包含空间信息和上下文信息的特征图像；其次，采用基于聚类的方法进行共显著性策略，将最终的差异图像与各时间点的特征差异图像结合，直接突出变化的共同区域；最后，采用模糊局部信息c均值聚类算法（FLICM）和决策投票法提取实际变化图。实验结果表明，本文提出的方法在高分辨率遥感图像的变化检测中具有优异的性能。
Change detection for remote sensing image is of great significance to a diverse range of applications. From the point of object-based method, this paper provides a change detection algorithm based on co-saliency strategy for multitemporal high resolution images. Firstly, the final difference image fused by difference feature and log difference feature, is generated, and feature image including spatial and contextual information is obtained by Gabor wavelet transform. Secondly, co-saliency strategy is performed via cluster-based method, combining the final difference image with feature difference image at each temporal data, and highlighting the common regions as the changed directly. Finally, actual change map is extracted by fuzzy local information C-means clustering algorithm (FLICM) and decision-voted method. The experiments show the method proposed in this paper has a superior performance in change detection for high resolution remote sensing images.