Abstract
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.
Abstract (translated)
土地覆盖图是地球观测中一个至关重要的应用，旨在对每个像素的遥感图像中的土地覆盖类型进行分类。由于自然和人类活动会改变地貌，土地覆盖图需要快速更新。然而，在现有分类系统中发现新出现的土地覆盖类型仍然是一个困难的任务，受到各种规模复杂陆地对象和缺乏在整个地理区域内足够的标记数据的影响。在本文中，我们提出了一个基于通用的少样本分割框架，名为SegLand，用于更新高分辨率土地覆盖图中的新类。具体来说，所提出的框架分为三个部分：（a）数据预处理：对基训练集和新类支持集的数据进行分析和增强；（b）混合分割结构；将多个基学习器和一种经过修改的投影 onto 异面原型（POP）网络相结合，以增强基类识别并从不足的标记数据中挖掘新类；（c）终极融合：将基学习器和POP网络的语义分割结果进行合理的融合。所提出的框架在OpenEarthMap Land Cover Mapping Few-Shot Challenge的排行榜上获得了第一。实验结果表明，该框架可以自动用有限标记数据更新新土地覆盖类的实例。
URL
https://arxiv.org/abs/2404.12721
