Land-cover mapping is one of the vital applications in Earth observation, aiming at classifying each pixel's land-cover type of remote-sensing images. As natural and human activities change the landscape, the land-cover map needs to be rapidly updated. However, discovering newly appeared land-cover types in existing classification systems is still a non-trivial task hindered by various scales of complex land objects and insufficient labeled data over a wide-span geographic area. In this paper, we propose a generalized few-shot segmentation-based framework, named SegLand, to update novel classes in high-resolution land-cover mapping. Specifically, the proposed framework is designed in three parts: (a) Data pre-processing: the base training set and the few-shot support sets of novel classes are analyzed and augmented; (b) Hybrid segmentation structure; Multiple base learners and a modified Projection onto Orthogonal Prototypes (POP) network are combined to enhance the base-class recognition and to dig novel classes from insufficient labels data; (c) Ultimate fusion: the semantic segmentation results of the base learners and POP network are reasonably fused. The proposed framework has won first place in the leaderboard of the OpenEarthMap Land Cover Mapping Few-Shot Challenge. Experiments demonstrate the superiority of the framework for automatically updating novel land-cover classes with limited labeled data.

翻译：土地覆盖制图是地球观测中的重要应用之一，旨在对遥感影像中每个像素的土地覆盖类型进行分类。随着自然和人类活动改变地表景观，土地覆盖图需要快速更新。然而，在现有分类体系中发现新出现的土地覆盖类型仍是一项艰巨任务，主要受限于复杂地物尺度的多样性以及广域地理范围内标注数据的不足。本文提出一种基于广义少样本分割的框架SegLand，用于更新高分辨率土地覆盖制图中的新类别。具体而言，该框架由三部分组成：(a) 数据预处理：对基础训练集和新类别的少样本支持集进行分析与增强；(b) 混合分割结构：结合多个基础学习器与改进的正交原型投影网络，增强基础类别识别能力并从稀缺标注数据中挖掘新类别；(c) 最终融合：合理融合基础学习器与正交原型投影网络的语义分割结果。所提框架在OpenEarthMap土地覆盖制图少样本挑战赛排行榜中荣获第一名。实验表明，该框架在有限标注数据条件下自动更新新土地覆盖类别方面具有优越性。