  Change detection in heterogeneous remote sensing images is crucial for
disaster damage assessment. Recent methods use homogenous transformation, which
transforms the heterogeneous optical and SAR remote sensing images into the
same feature space, to achieve change detection. Such transformations mainly
operate on the low-level feature space and may corrupt the semantic content,
deteriorating the performance of change detection. To solve this problem, this
paper presents a new homogeneous transformation model termed deep homogeneous
feature fusion (DHFF) based on image style transfer (IST). Unlike the existing
methods, the DHFF method segregates the semantic content and the style features
in the heterogeneous images to perform homogeneous transformation. The
separation of the semantic content and the style in homogeneous transformation
prevents the corruption of image semantic content, especially in the regions of
change. In this way, the detection performance is improved with accurate
homogeneous transformation. Furthermore, we present a new iterative IST (IIST)
strategy, where the cost function in each IST iteration measures and thus
maximizes the feature homogeneity in additional new feature subspaces for
change detection. After that, change detection is accomplished accurately on
the original and the transformed images that are in the same feature space.
Real remote sensing images acquired by SAR and optical satellites are utilized
to evaluate the performance of the proposed method. The experiments demonstrate
that the proposed DHFF method achieves significant improvement for change
detection in heterogeneous optical and SAR remote sensing images, in terms of
both accuracy rate and Kappa index.
