Existing structure-analysis-based algorithms of building detection in high-resolution synthetic aperture radar (SAR) images only consider straight-line and L-shaped buildings and utilize the shadowed areas of the highlighted lines on buildings in the detection process. In complicated scenes

shadowed areas cannot be accurately detected by restricting dark backgrounds and dense buildings;this condition results in large errors and low accuracy in building detection. Focusing on these problems

an algorithm based on the morphological hierarchical analysis of unsupervised building detection in high-resolution SAR images is developed in this study. The method is applied to single-polarization high-resolution SAR images. First

an improved alternating sequential filter (ASF) (i.e.

extended ASF) is utilized to reduce the inherent speckle noise and eliminate the interference of background noise in the homogeneous regions significantly. Second

the differential morphological attribute profiles are calculated to implement the geometric structure feature extraction of buildings in a SAR image in a complex scene. Finally

post-processing methods

such as feature fusion and threshold segmentation

are performed to obtain intricate building information. Compared with other structure-analysis-based algorithms

the proposed method exhibits a higher detection rate and a smaller error for an urban area with high-density buildings. The precision and recall rates of the proposed method are 95.38% and 86.31%

respectively. The method also results in reduced false-alarm rates. The improvement and combination of ASFs and morphological attribute profiles are suitable for the detection of high-density buildings with different directions

sizes

and shapes.