Large-scale crop mapping is an important task in agricultural resource monitoring, but it does usually require the ground-truth labels of all the land-cover types in the remotely sensed imagery. However, labeling each land-cover type is time-consuming and labor-intensive. One-class classification, which only needs samples of the class of interest, can solve the problem of redundant labeling. However, the traditional one-class classifiers require well-designed features to realize fine classification, and are thus difficult to apply to complex multi-modal remote sensing data, i.e., optical imagery and synthetic aperture radar (SAR) imagery. In this paper, a deep one-class crop (DOCC) framework that includes a deep one-class crop extraction module and a one-class crop extraction loss module is proposed for large-scale one-class crop mapping. The DOCC framework takes only the samples of one target class as the input to extract the crop of interest by positive and unlabeled learning and can automatically extract the features for one-class crop mapping, without requiring a large amount of labeling for all the land-cover type or feature design based on prior expert knowledge. Experiments conducted on multi-modal remote sensing data, i.e., Zhuhai-1 hyperspectral satellite data, Sentinel-2 multispectral time-series satellite data and Sentinel-1 SAR satellite data, illustrate that DOCC can automatically extract the effective features for one-class classification from multi-modal satellite imagery and reaches the highest F1 accuracy compared with other methods on respective satellite imagery. The results also reveal the different performance of multi-modal satellite imagery when they are used to extract different crop types. Meanwhile, the feasibility of DOCC for multi-modal data can be beneficial for large-scale mapping under different conditions when the samples of multi-class are difficult to obtain.

大规模作物制图是农业资源监测中的一项重要任务，但它通常需要遥感图像中所有土地覆盖类型的地面实况标签。然而，标记每种土地覆盖类型既费时又费力。一类分类，只需要感兴趣的类的样本，可以解决冗余标注的问题。然而，传统的一类分类器需要精心设计的特征来实现精细分类，因此难以应用于复杂的多模态遥感数据，即光学影像和合成孔径雷达（SAR）影像。在本文中，提出了一种深度一类作物（DOCC）框架，该框架包括深度一类作物提取模块和一类作物提取损失模块，用于大规模一类作物制图。DOCC框架仅以一个目标类的样本为输入，通过正向和无标记学习提取感兴趣的作物，可以自动提取特征进行一类作物制图，不需要对所有土地进行大量的标注——基于先验专家知识的封面类型或特征设计。对多模态遥感数据，即珠海一号高光谱卫星数据、哨兵二号多光谱时间序列卫星数据和哨兵一号SAR卫星数据进行的实验表明，DOCC可以自动提取有效特征进行一类分类来自多模态卫星图像，并在各自的卫星图像上与其他方法相比达到最高的 F1 精度。结果还揭示了多模态卫星图像在用于提取不同作物类型时的不同性能。同时，DOCC对于多模态数据的可行性，有利于在多类样本难以获取的情况下进行不同条件下的大规模映射。