In supervised deep learning, learning good representations for remote--sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self--supervised learning shows its outstanding capability to learn representations of images, especially the methods of instance discrimination. Comparing methods of instance discrimination, clustering--based methods not only view the transformations of the same image as ``positive" samples but also similar images. In this paper, we propose a new clustering-based method for representation learning. We first introduce a quantity to measure representations' discriminativeness and from which we show that even distribution requires the most discriminative representations. This provides a theoretical insight into why evenly distributing the images works well. We notice that only the even distributions that preserve representations' neighborhood relations are desirable. Therefore, we develop an algorithm that translates the outputs of a neural network to achieve the goal of evenly distributing the samples while preserving outputs' neighborhood relations. Extensive experiments have demonstrated that our method can learn representations that are as good as or better than the state of the art approaches, and that our method performs computationally efficiently and robustly on various RSI datasets.
翻译:在有监督的深层学习中,学习遥感图像的良好表现取决于手动说明。然而,在遥感领域,很难获得大量贴标签的数据。最近,自我监督的学习表明,它非常有能力了解图像的表现形式,特别是实例歧视的方法。比较实例歧视的方法,基于集群的方法不仅观察“积极”样本的图像变化情况,而且还观察类似图像。在本文中,我们提议一种新的基于集群的演示学习方法。我们首先引入数量来衡量演示的偏向性,从中我们可以看出,甚至分布都需要最有区别的表述。这从理论上揭示了以均衡方式传播图像为什么效果良好。我们注意到,只有维护图像邻居关系的均衡分布才是可取的。因此,我们开发了一种算法,将神经网络的输出结果转化为平衡分配样本的目标,同时保持产出的邻居关系。广泛的实验表明,我们的方法可以学习好于或好于艺术状态的表述。我们的方法在计算上能够有效地进行各种数据分析。