Research advances and challenges in multi-label classification of remote sensing images
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摘要: 多标签遥感图像分类是遥感分析领域的基础研究任务之一,解析给定的遥感图像并识别其中的类别语义,可以为下游计算机视觉任务提供重要的技术基础; 由于遥感图像空间分辨率不断提升,众多遥感对象以不同规模、颜色、形状分布于图像的各个区域,为遥感图像多标签分类任务带来了严峻挑战。该文聚焦于遥感领域的多标签图像分类研究,对该问题的前沿研究进展进行总结分析。首先,阐述多标签遥感图像分类任务的问题定义,并对该研究问题中常用的多标签图像数据集和模型评估指标进行归纳介绍; 进而,对该领域的前沿进展进行系统性的介绍,深入剖析多标签遥感图像分类过程中的2个关键任务——遥感图像特征提取和标签特征提取; 最后,针对遥感图像特性,分析了该任务当前存在的挑战和问题,并对未来研究方向进行展望。Abstract: Multi-label classification of remote sensing images plays a fundamental role in remote sensing analysis. Parsing given remote sensing images to identify semantic labels can provide a significant technical basis for downstream computer vision tasks. With the continuously improved spatial resolution of remote sensing images, many remote sensing objects with different scales, colors, and shapes are distributed in various zones of images, posing high challenges to the multi-label classification task of remote sensing images. This study focuses on the multi-label classification of images in the field of remote sensing, summarizing and analyzing the frontier research advances in this regard. First of all, this study expounded the problem definition for the multi-label classification task of remote sensing images while generalizing the commonly used multi-label image datasets and model evaluation indicators. Furthermore, by systematically presenting the frontier progress in this field, this study delved into two key tasks in the multi-label classification of remote sensing images: feature extraction of remote sensing images and label feature extraction. Finally, based on the characteristics of remote sensing images, this study analyzed the current challenges of multi-label classification as well as subsequent research orientation.
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