  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.
