利用局部稀疏不变特征的遥感影像检索

Remote sensing image retrieval based on sparse local invariant features

为了增强遥感影像局部特征的表征能力并充分利用过完备字典的稀疏分解,提出了基于稀疏表示特征构建视觉词典的遥感影像检索新方法。首先,提取遥感训练影像库的局部不变特征,对大量的局部特征训练过完备字典并将在该字典更新下获取的稀疏表示作为图像的特征描述;然后,对稀疏表示特征构建视觉词典,并进行空间金字塔匹配,获取稀疏直方图特征;最后,使用稀疏特征训练SVM分类模型,通过分类模型输出与查询影像属于一个类别的影像,在该类别的影像集中进行相似度匹配,返回与查询影像最为相似的图像,实现检索。实验结果表明,新方法提取的特征不仅具备局部不变特征的鲁棒性,还提供了必要的语义信息,在影像检索领域具有较强的实用性和适用性。

In order to enhance the capability of local feature representation of remote sensing images and to make full use of sparse decomposition of over-complete dictionary,this paper proposes a new method of remote sensing image retrieval based on sparse representation feature. In this method first the local invariant features are extracted from the training remote sensing image database and trains over-complete dictionary based on local features,and thus the sparse representation will be obtained under the dictionary update; the authors regard the sparse representation as the image＇s final feature description. Secondly,the authors construct a visual dictionary using sparse representation features,and obtain the sparse histograms by spatial pyramid matching algorithm. Finally,the SVM classification model is trained based on the sparse features,by using the classification model,the images classified as one category with the query image to be output. The similarity matching is carried out in the output image set,and an image with the largest similarity is returned to achieve the image retrieval. Experimental result shows that the features extracted by the new method not only possess the robustness of local invariant features but also provide the necessary semantic information,which is of great practicality and applicability in image retrieval research field.