Land cover classification has achieved significant advances by employing deep convolutional network (ConvNet) based methods. Following the paradigm of learning deep models, land cover classification is modeled as semantic segmentation of very high resolution remote sensing images. In order to obtain accurate segmentation results, high-level categorical semantics and low-level spatial details should be effectively fused. To this end, we propose a novel bidirectional gird fusion network to aggregate the multilevel features across the ConvNet. Specifically, the proposed model is characterized by a bidirectional fusion architecture, which enriches diversity of feature interaction by encouraging bidirectional information flow. In this way, our model gains mutual benefits between top–down and bottom–up information flows. Moreover, a grid fusion architecture is then followed for further feature refinement in a dense and hierarchical fusion manner. Finally, effective feature upsampling is also critical for the multiple fusion operations. Consequently, a content-aware feature upsampling kernel is incorporated for further improvement. Our whole model consistently achieves significant improvement over state-of-the-art methods on two major datasets, ISPRS and GID.

中文翻译：

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用于高分辨率遥感图像精确土地覆盖分类的双向网格融合网络

通过采用基于深度卷积网络 (ConvNet) 的方法，土地覆盖分类取得了重大进展。遵循学习深度模型的范式，土地覆盖分类被建模为超高分辨率遥感图像的语义分割。为了获得准确的分割结果，高级分类语义和低级空间细节应该有效融合。为此，我们提出了一种新颖的双向网格融合网络来聚合 ConvNet 上的多级特征。具体来说，所提出的模型的特点是双向融合架构，它通过鼓励双向信息流来丰富特征交互的多样性。通过这种方式，我们的模型在自上而下和自下而上的信息流之间获得了互惠互利。而且，然后遵循网格融合架构以密集和分层融合的方式进一步细化特征。最后，有效的特征上采样对于多重融合操作也很关键。因此，内容感知功能上采样内核被纳入进一步改进。我们的整个模型在两个主要数据集 ISPRS 和 GID 上始终比最先进的方法取得显着改进。