  Change detection is one of the fundamental applications of synthetic aperture
radar (SAR) images. However, speckle noise presented in SAR images has a much
negative effect on change detection. In this research, a novel two-phase
object-based deep learning approach is proposed for multi-temporal SAR image
change detection. Compared with traditional methods, the proposed approach
brings two main innovations. One is to classify all pixels into three
categories rather than two categories: unchanged pixels, changed pixels caused
by strong speckle (false changes), and changed pixels formed by real terrain
variation (real changes). The other is to group neighboring pixels into
segmented into superpixel objects (from pixels) such as to exploit local
spatial context. Two phases are designed in the methodology: 1) Generate
objects based on the simple linear iterative clustering algorithm, and
discriminate these objects into changed and unchanged classes using fuzzy
c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the
set of changed and unchanged superpixels. 2) Deep learning on the pixel sets
over the changed superpixels only, obtained in the first phase, to discriminate
real changes from false changes. SLIC is employed again to achieve new
superpixels in the second phase. Low rank and sparse decomposition are applied
to these new superpixels to suppress speckle noise significantly. A further
clustering step is applied to these new superpixels via FCM. A new PCANet is
then trained to classify two kinds of changed superpixels to achieve the final
change maps. Numerical experiments demonstrate that, compared with benchmark
methods, the proposed approach can distinguish real changes from false changes
effectively with significantly reduced false alarm rates, and achieve up to
99.71% change detection accuracy using multi-temporal SAR imagery.
