Deep learning networks have facilitated the automated scene recognition of landforms based on geomorphogenesis. However, current genetic landform classification methods do not consider regional geological context, which can more accurately reflect the formation and evolution mechanism of geomorphic landforms than local ones. Therefore, this study proposes a multimodal, deep learning landform recognition framework based on a joint contextual geological and channel attention module (GCMENET). First, the multibranch feature extraction network of DenseNet121 is used to extract the respective features from the target scene and the contextual geological scene. Second, the features similar to the landform features of the target scene are extracted from the contextual geological features based on the cosine method and then combined with geomorphic features of the target scene. Third, channel attention mechanism is used to reduce the interference caused by redundant contextual geological information after fusion of data. To measure the classification accuracy of GCMENET, we establish a fine geomorphogenic dataset consisting of remote sensing images of six landform types with a $64\times64$ -pixel size and 10-m resolution (JOS10m). During the training process of two geomorphogenic datasets, the feature extraction network without batchnorm2d (batch normalization) could preserve the distribution and spatial alignment of data from the components. Using different training-to-validation data ratios and combinations of input components, the results of the GCMENET supplemented with the joint contextual geological and channel attention module exhibited greater accuracy than those obtained without the module. This observation confirms the importance of contextual geological information in automated geomorphogenic landforms.

中文翻译：

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基于多模态特征提取和区域地质背景的细粒度遗传地貌分类网络

深度学习网络促进了基于地貌发生的地貌自动场景识别。然而，目前的成因地貌分类方法没有考虑区域地质背景，相比局部地质地貌更能准确反映地貌地貌的形成和演化机制。因此，本研究提出了一种基于联合上下文地质和通道注意模块（GCMENET）的多模态、深度学习地形识别框架。首先，使用 DenseNet121 的多分支特征提取网络从目标场景和上下文地质场景中提取各自的特征。第二，基于余弦法从上下文地质特征中提取与目标场景的地貌特征相似的特征，然后与目标场景的地貌特征相结合。第三，通道注意力机制用于减少数据融合后冗余上下文地质信息造成的干扰。为了衡量GCMENET的分类精度，我们建立了一个由六种地貌类型的遥感图像组成的精细地貌数据集， $64\times64$ -像素大小和 10 米分辨率 (JOS10m)。在两个地貌数据集的训练过程中，没有batchnorm2d（batch normalization）的特征提取网络可以保留来自组件的数据的分布和空间对齐。使用不同的训练与验证数据比率和输入组件的组合，GCMENET 的结果与联合上下文地质和通道注意模块相辅相成，显示出比不使用该模块获得的结果更高的准确性。这一观察证实了上下文地质信息在自动地貌形成中的重要性。