  Building change detection is of great significance in high resolution remote
sensing applications. Multi-index learning, one of the state-of-the-art
building change detection methods, still has drawbacks like incapability to
find change types directly and heavy computation consumption of MBI. In this
paper, a two-stage building change detection method is proposed to address
these problems. In the first stage, a multi-scale filtering building index
(MFBI) is calculated to detect building areas in each temporal with fast speed
and moderate accuracy. In the second stage, images and the corresponding
building maps are partitioned into grids. In each grid, the ratio of building
areas in time T2 and time T1 is calculated. Each grid is classified into one of
the three change patterns, i.e., significantly increase, significantly decrease
and approximately unchanged. Exhaustive experiments indicate that the proposed
method can detect building change types directly and outperform the current
multi-index learning method.
