Conventional remote sensing image retrieval (RSIR) system usually performs single-label retrieval where each image is annotated by a single label representing the most significant semantic content of the image. In this scenario, however, the scene complexity of remote sensing images is ignored, where an image might have multiple classes (i.e., multiple labels), resulting in poor retrieval performance. We therefore propose a novel multilabel RSIR approach based on fully convolutional network (FCN). Specifically, FCN is first trained to predict segmentation map of each image in the considered image archive. We then obtain multilabel vector and extract region convolutional features of each image based on its segmentation map. The extracted region features are finally used to perform region-based multilabel retrieval. The experimental results show that our approach achieves state-of-the-art performance in contrast to handcrafted and convolutional neural network features.

中文翻译：

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基于全卷积网络的多标签遥感图像检索

传统的遥感图像检索 (RSIR) 系统通常执行单标签检索，其中每个图像都由一个表示图像最重要语义内容的标签进行注释。然而，在这种情况下，忽略了遥感图像的场景复杂性，其中一幅图像可能有多个类（即多个标签），导致检索性能不佳。因此，我们提出了一种基于完全卷积网络（FCN）的新型多标签 RSIR 方法。具体来说，首先训练 FCN 来预测所考虑图像档案中每个图像的分割图。然后我们获得多标签向量并根据其分割图提取每个图像的区域卷积特征。提取的区域特征最终用于执行基于区域的多标签检索。