Land cover classification (LCC) is an important application in remote sensing data interpretation and invariably faces big intra-class variance and sample imbalance in remote sensing images. The optical image is obtained by satellites capturing the spectral information of the Earth’s surface, and the synthetic aperture radar (SAR) image is produced by the satellite actively transmitting and receiving the electromagnetic wave signals reflected from land covers. Because of the limitations of the optical image, a single modality (optical image) might be disturbed by external conditions, especially complex weather. Using heterogeneous SAR and optical images for LCC can reduce the negative impact caused by single-modal data damage, and multi-modal data can also be used as supplementary information to enhance classification accuracy. However, general LCC methods mainly focus on remote sensing data of a single modality without fully considering the multi-modalities of land covers. Therefore, we propose a dual-stream deep high-resolution network (DDHRNet) to deeply integrate SAR and optical data at the feature level in every branch. The network can effectively exploit the complementary information in heterogeneous images. It improves classification performance and achieves significant improvements in the classification of clouded images. A multi-modal squeeze-and-excitation (MSE) module is also utilized to fuse the features. Compared with the ordinary methods, MSE modules can lead to an improvement of about 1% to 5% in overall accuracy (OA), Kappa coefficients, and mean intersection over union (mIoU). Besides, in order to evaluate our method, we describe in detail the preprocessing process of Gaofen-2 (GF2) and Gaofen-3 (GF3) data before they are used in the LCC task. The experiments show that the proposed method performs well compared with other excellent segmentation methods and obtains the best performance on heterogeneous images from GF2 and GF3. The code and datasets are available at: https://github.com/XD-MG/DDHRNet.

土地覆盖分类（LCC）是遥感数据解释中的重要应用，在遥感图像中总是面临较大的类内方差和样本不平衡。光学图像是由卫星捕获地球表面的光谱信息获得的，合成孔径雷达（SAR）图像是由卫星主动发射和接收地表反射的电磁波信号产生的。由于光学图像的局限性，单一模态（光学图像）可能会受到外部条件的干扰，尤其是复杂的天气。使用异构 SAR 和光学图像进行 LCC 可以减少单模态数据损坏带来的负面影响，同时多模态数据也可以作为补充信息来提高分类精度。然而，一般LCC方法主要关注单一模态的遥感数据，没有充分考虑土地覆盖的多模态。因此，我们提出了一种双流深度高分辨率网络（DDHRNet），以在每个分支的特征级别上深度集成 SAR 和光学数据。该网络可以有效地利用异构图像中的互补信息。它提高了分类性能，并在云图像的分类方面取得了显着的进步。多模态挤压和激发 (MSE) 模块也用于融合特征。与普通方法相比，MSE 模块可以在总体准确率（OA）、Kappa 系数和联合平均交集（mIoU）方面提高约 1% 到 5%。此外，为了评估我们的方法，我们详细描述了高分2（GF2）和高分3（GF3）数据在用于LCC任务之前的预处理过程。实验表明，与其他优秀的分割方法相比，所提出的方法表现良好，并且在来自 GF2 和 GF3 的异构图像上获得了最佳性能。代码和数据集位于：https://github.com/XD-MG/DDHRNet。