论文标题
在农业空中图像的语义分割中的增强不变性和自适应采样
Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images
论文作者
论文摘要
在本文中，我们研究了农业空中图像的语义分割问题。我们观察到该任务所使用的现有方法是设计的，而没有考虑航空数据的两个特征：（i）自上而下的透视图意味着该模型不能依靠场景的固定语义结构，因为相同的场景可以在传感器的不同旋转中经历； （ii）语义类别的分布可能会有很大的失衡，因为场景的相关对象可能以极大不同的尺度出现（例如，农作物和小型车辆）。我们基于两个想法提出了解决这些问题的解决方案：（i）我们一起使用一组合适的增强和一致性损失来指导模型学习语义表示，这些语义表示是上自上而下透视图（增强不变性）典型的光度和几何变化的； （ii）我们使用采样方法（自适应抽样），该方法基于类像素的分布和实际网络置信度的量度选择训练图像。通过在农业视频数据集上进行的一系列实验集，我们证明了我们提出的策略可以改善当前最新方法的性能。
In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery. We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down perspective implies that the model cannot rely on a fixed semantic structure of the scene, because the same scene may be experienced with different rotations of the sensor; (ii) there can be a strong imbalance in the distribution of semantic classes because the relevant objects of the scene may appear at extremely different scales (e.g., a field of crops and a small vehicle). We propose a solution to these problems based on two ideas: (i) we use together a set of suitable augmentation and a consistency loss to guide the model to learn semantic representations that are invariant to the photometric and geometric shifts typical of the top-down perspective (Augmentation Invariance); (ii) we use a sampling method (Adaptive Sampling) that selects the training images based on a measure of pixel-wise distribution of classes and actual network confidence. With an extensive set of experiments conducted on the Agriculture-Vision dataset, we demonstrate that our proposed strategies improve the performance of the current state-of-the-art method.
