论文标题
密集扩张的卷积合并网络进行土地覆盖分类
Dense Dilated Convolutions Merging Network for Land Cover Classification
论文作者
论文摘要
遥感图像的土地覆盖分类是一项具有挑战性的任务，这是由于注释的数据有限，高度不平衡的类，频繁的不正确像素级注释以及语义分割任务中固有的复杂性。在本文中，我们提出了一种新颖的架构，称为“密集的卷积合并网络（DDCM-net）”来解决此任务。拟议的DDCM-NET由密集的扩张图像卷积和变化速率合并。与遥感域中的最新方法相比，这有效地利用了扩大网络的接收场的丰富组合，以更少的参数和功能扩大了网络的接受场。重要的是，DDCM-NET获得了融合的局部和全球范围信息，实际上结合了具有非常高分辨率的空中图像中具有相似颜色和纹理的多尺度和复杂形对象的歧视能力。我们证明了拟议的DDCM-NET在公开可用的ISPRS Potsdam和Vaihingen数据集以及DeepGlobe土地覆盖数据集上的有效性，鲁棒性和灵活性。我们的单个模型是在三波斯坦和Vaihingen数据集中训练的，与其他经过超过三波段数据训练的已发表的模型相比，在平均值相交（MIOU）和F1得分方面，取得了更好的准确性。我们进一步验证了我们的Deepglobe数据集的模型，与最近的相关工作相比，参数少得多，计算成本较低，计算成本较低。代码可在https://github.com/samleoqh/ddcm-semantic-segmentation-pytorch中找到
Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task. The proposed DDCM-Net consists of dense dilated image convolutions merged with varying dilation rates. This effectively utilizes rich combinations of dilated convolutions that enlarge the network's receptive fields with fewer parameters and features compared with the state-of-the-art approaches in the remote sensing domain. Importantly, DDCM-Net obtains fused local- and global-context information, in effect incorporating surrounding discriminative capability for multiscale and complex-shaped objects with similar color and textures in very high-resolution aerial imagery. We demonstrate the effectiveness, robustness, and flexibility of the proposed DDCM-Net on the publicly available ISPRS Potsdam and Vaihingen data sets, as well as the DeepGlobe land cover data set. Our single model, trained on three-band Potsdam and Vaihingen data sets, achieves better accuracy in terms of both mean intersection over union (mIoU) and F1-score compared with other published models trained with more than three-band data. We further validate our model on the DeepGlobe data set, achieving state-of-the-art result 56.2% mIoU with much fewer parameters and at a lower computational cost compared with related recent work. Code available at https://github.com/samleoqh/DDCM-Semantic-Segmentation-PyTorch
