  Change detection has been a hotspot in remote sensing technology for a long
time. With the increasing availability of multi-temporal remote sensing images,
numerous change detection algorithms have been proposed. Among these methods,
image transformation methods with feature extraction and mapping could
effectively highlight the changed information and thus has better change
detection performance. However, changes of multi-temporal images are usually
complex, existing methods are not effective enough. In recent years, deep
network has shown its brilliant performance in many fields including feature
extraction and projection. Therefore, in this paper, based on deep network and
slow feature analysis (SFA) theory, we proposed a new change detection
algorithm for multi-temporal remotes sensing images called Deep Slow Feature
Analysis (DSFA). In DSFA model, two symmetric deep networks are utilized for
projecting the input data of bi-temporal imagery. Then, the SFA module is
deployed to suppress the unchanged components and highlight the changed
components of the transformed features. The CVA pre-detection is employed to
find unchanged pixels with high confidence as training samples. Finally, the
change intensity is calculated with chi-square distance and the changes are
determined by threshold algorithms. The experiments are performed on two
real-world datasets and a public hyperspectral dataset. The visual comparison
and quantitative evaluation have both shown that DSFA could outperform the
other state-of-the-art algorithms, including other SFA-based and deep learning
methods.
