Machine learning for remote sensing imaging relies on up-to-date and accurate labels for model training and testing. Labelling remote sensing imagery is time and cost intensive, requiring expert analysis. Previous labelling tools rely on pre-labelled data for training in order to label new unseen data. In this work, we define an unsupervised pipeline for finding and labelling geographical areas of similar context and content within Sentinel-2 satellite imagery. Our approach removes limitations of previous methods by utilising segmentation with convolutional and graph neural networks to encode a more robust feature space for image comparison. Unlike previous approaches we segment the image into homogeneous regions of pixels that are grouped based on colour and spatial similarity. Graph neural networks are used to aggregate information about the surrounding segments enabling the feature representation to encode the local neighbourhood whilst preserving its own local information. This reduces outliers in the labelling tool, allows users to label at a granular level, and allows a rotationally invariant semantic relationship at the image level to be formed within the encoding space. Our pipeline achieves high contextual consistency, with similarity scores of SSIM = 0.96 and SAM = 0.21 under context-aware evaluation, demonstrating robust organisation of the feature space for interactive labelling.

翻译：遥感影像的机器学习依赖于最新且准确的标签进行模型训练与测试。遥感影像标注工作耗时耗力，需要专家分析。以往的标注工具依赖预标注数据进行训练，以标注新的未见数据。本研究定义了一种无监督流程，用于在Sentinel-2卫星影像中发现并标注具有相似上下文与内容的地理区域。我们的方法通过结合卷积神经网络与图神经网络的图像分割技术，构建了更鲁棒的图像特征比较空间，从而突破了传统方法的局限。与先前方法不同，我们将图像分割为基于色彩与空间相似性聚类的同质像素区域，并利用图神经网络聚合周边区域信息，使特征表示既能编码局部邻域信息，又能保持自身局部特征。这降低了标注工具的异常值干扰，支持用户进行细粒度标注，并在编码空间内构建了图像层级的旋转不变语义关系。我们的流程实现了较高的上下文一致性，在上下文感知评估中取得SSIM=0.96与SAM=0.21的相似度评分，证明了该特征空间组织方式对交互式标注具有鲁棒性。