  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.
