论文标题
基于相关的融合
Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
论文作者
论文摘要
将多个时间场景的土地使用类别分类并检测其语义场景级别的变化以覆盖城市地区的图像，可以直接反映土地使用的过渡。现有场景变化检测的方法很少关注双期特征的时间相关性，并且主要在小型场景更改检测数据集上进行评估。在这项工作中，我们提出了一个相关模块，该模块融合了双颞型嵌入中高度相关的组件。我们首先提取具有深卷积网络的双向输入的深度表示。然后，提取的功能将投影到较低的维度空间中，以计算实例级别的相关性。跨时期融合将根据相关模块中的计算相关性执行。最终场景分类是通过软磁性激活层获得的。在目标函数中，我们引入了一种用于计算时间相关性的新公式。本文还给出了提出的模块的返回梯度的详细推导。此外，我们提出了更大的规模场景更改检测数据集，并在此数据集上进行了实验。实验结果表明，我们提出的相关模块可以显着改善多时间场景分类和场景变化检测结果。
Classifying multi-temporal scene land-use categories and detecting their semantic scene-level changes for imagery covering urban regions could straightly reflect the land-use transitions. Existing methods for scene change detection rarely focus on the temporal correlation of bi-temporal features, and are mainly evaluated on small scale scene change detection datasets. In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings. We firstly extracts the deep representations of the bi-temporal inputs with deep convolutional networks. Then the extracted features will be projected into a lower dimension space to computed the instance-level correlation. The cross-temporal fusion will be performed based on the computed correlation in CorrFusion module. The final scene classification are obtained with softmax activation layers. In the objective function, we introduced a new formulation for calculating the temporal correlation. The detailed derivation of backpropagation gradients for the proposed module is also given in this paper. Besides, we presented a much larger scale scene change detection dataset and conducted experiments on this dataset. The experimental results demonstrated that our proposed CorrFusion module could remarkably improve the multi-temporal scene classification and scene change detection results.
