遥感图像存在大量语义对象,相同的语义对象视觉差异较大,针对卷积神经网络(CNN)提取的全局特征不能准确描述图像内容的问题,提出了一种使用区域注意力机制的遥感图像检索方法。首先去除CNN的全连接层,将高层特征作为区域注意力网络的输入;然后在遥感图像数据集上分别训练CNN和区域注意力网络,提取具有区域关注度的图像特征;最后构建了一种多距离相似性度量矩阵并采用扩展查询以提高检索性能。实验结果表明,相比基于全局特征的遥感图像检索方法,本方法能有效抑制遥感图像背景和不相关的图像区域,在两大遥感实验数据集上的检索性能更好。

Remote sensing images have a large number of semantic objects, and the visual differences of the same semantic objects are large. Aiming at the problem that the global features extracted by convolutional neural network (CNN) cannot accurately describe the image content, a remote sensing image retrieval method based on regional attention mechanism is proposed. First, the fully connected layer of the CNN is removed, and the deep features are used as the input of regional attention network. Then, the CNN and regional attention network are trained respectively on remote sensing image dataset. After that, local image features with attention can be extracted. Finally, a multi-distance similarity metric matrix is constructed, and extended query is used to improve retrieval performance. Experimental results show that, compared with remote sensing image retrieval method based on global features, this method can effectively suppress the background of remote sensing images and unrelated image regions, and the retrieval performance is better on the two large remote sensing experimental data sets.