HSLabeling: Toward Efficient Labeling for Large-Scale Remote Sensing Image Segmentation With Hybrid Sparse Labeling

Abstract

Dense pixel-wise labeling of large-scale remote sensing images (RSI) is very time-consuming, while sparse labels (i.e., points, scribbles, or blocks) can be an efficient way to reduce labeling costs. Most existing sparse label-based methods adopt only one type of label for image segmentation, which cannot reflect the complex land covers in the RSI for training the model, thus leading to inferior segmentation performance. We observe that land covers with different shapes and complexity can be optimally represented by different sparse labels. Inspired by this observation, we propose a novel sparse labeling framework, termed Hybrid Sparse Labeling (HSLabeling), for large-scale RSI segmentation. Our HSLabeling can adaptively select the optimal hybrid sparse labels for different land covers, according to labeling cost and segmentation contribution of different sparse labels. Specifically, we first propose a label segmentation contribution information estimation module that estimates the information of different sparse labels according to the diversity and shape of land covers. After that, we propose an Optimal Hybrid Labeling Strategy (OHLS) to assign optimal types of labels for different land covers. In the OHLS, label assignment is formulated as an optimization problem that trades off label segmentation contribution information and labeling cost. We employ the greedy algorithm to efficiently solve the optimization problem and adaptively assign labels for varied land covers. Extensive experiments on three large-scale RSI datasets have demonstrated that our HSLabeling achieves almost fully supervised performance with extremely low labeling costs. In addition, compared with the single type sparse label, HSLabeling can also utilize much lower labeling costs to obtain the same performance. The source code is available at https://github.com/linjiaxing99/HSLabeling.