Abstract
Abstract. In SAR image interpretation, aircrafts are the important targets arousing much attention. However, it is far from easy to segment an aircraft from the background completely and precisely in SAR images. Because of the complex structure, different kinds of electromagnetic scattering take place on the aircraft surfaces. As a result, aircraft targets usually appear to be inhomogeneous and disconnected. It is a good idea to extract an aircraft target by the active shape model (ASM), since combination of the geometric information controls variations of the shape during the contour evolution. However, linear dimensionality reduction, used in classic ACM, makes the model rigid. It brings much trouble to segment different types of aircrafts. Aiming at this problem, an improved ACM based on ISOMAP is proposed in this paper. ISOMAP algorithm is used to extract the shape information of the training set and make the model flexible enough to deal with different aircrafts. The experiments based on real SAR data shows that the proposed method achieves obvious improvement in accuracy.
Highlights
As the development of SAR technology, imaging technology becomes much more mature
A feasible way is that the detection algorithm is implemented firstly to generate ROIs which contain aircrafts, and further processing will be carried out based the ROIs
Due to our limited SAR data capacity of aircraft targets and the lack of ground truth, which are the common problems of many other researchers, the shape training set cannot be established based on SAR images
Summary
As the development of SAR technology, imaging technology becomes much more mature. High resolution SAR images bring much convenience to automatic target recognition (ATR), because more structure and geometry details can be displayed. This is not suitable for actual application. Due to our limited SAR data capacity of aircraft targets and the lack of ground truth, which are the common problems of many other researchers, the shape training set cannot be established based on SAR images. To solve this problem, a series of vertical views of aircrafts are collected from the Internet. The shape model is consist of 23 control points that represent the stationary local features respectively
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