Benchmark datasets are critical for developing, evaluating, and comparing remote sensing image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in that (1) they were originally collected for land use/land cover classification instead of RSIR; (2) they are relatively small in terms of the number of classes as well as the number of images per class which makes them unsuitable for developing deep learning based approaches; and (3) they are not appropriate for RSIR due to the large amount of background present in the images. These limitations restrict the development of novel approaches for RSIR, particularly those based on deep learning which require large amounts of training data. We therefore present a new large-scale remote sensing dataset termed “PatternNet” that was collected specifically for RSIR. PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class. Significantly, PatternNet’s large scale makes it suitable for developing novel, deep learning based approaches for RSIR. We use PatternNet to evaluate the performance of over 35 RSIR methods ranging from traditional handcrafted feature based methods to recent, deep learning based ones. These results serve as a baseline for future research on RSIR.

基准数据集对于开发，评估和比较遥感图像检索（RSIR）方法至关重要。但是，当前的基准数据集的不足之处在于：（1）最初收集它们的目的是为了土地利用/土地覆被分类，而不是RSIR；（2）在类数和每类图像的数量上相对较小，这使其不适合开发基于深度学习的方法；（3）由于图像中存在大量背景，因此不适用于RSIR。这些局限性限制了RSIR新颖方法的开发，尤其是那些基于深度学习的方法，这些方法需要大量的训练数据。因此，我们提出了一个新的大规模遥感数据集，称为“ PatternNet”，该数据集是专门为RSIR收集的。PatternNet是从高分辨率图像中收集的，包含38个类，每个类800个图像。值得注意的是，PatternNet的大规模规模使其适合开发新颖的，基于深度学习的RSIR方法。我们使用PatternNet评估了超过35种RSIR方法的性能，这些方法范围从传统的基于手工特征的方法到最近的基于深度学习的方法。这些结果为RSIR的未来研究提供了基准。