With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two publicly available remote sensing image retrieval datasets and show that our method significantly outperforms state-of-the-art.
翻译:随着遥感图像数据迅速增长,管理和利用这些数据的高效和高效图像检索工具的需求很大。在本信中,我们介绍了基于内容的遥感图像检索新颖方法,其依据是Triplet 深超学习神经网络(CNN ) 。通过建立一个具有衡量学习客观功能的三联网络,我们提取了图像在一个语义空间中的代表性特征,在这个空间中,同一类图像彼此接近,而不同类别图像则相距甚远。在这样一个语义空间中,可以直接使用像Euclidean距离这样的简单度量度测量方法来比较图像的相似性,并有效检索同一类图像。我们还调查了一种受监督和不受监督的学习方法,以减少所学的语义特征的维度。我们介绍了两个公开提供的遥感图像检索数据集的全面实验结果,并表明我们的方法大大超越了最新技术。