Abstract
Target detection in foliage-penetrating (FOPEN), ultrawideband synthetic aperture radar (UWB SAR) images is a challenging problem. Given the low signal-to-clutter ratio, conventional detection algorithms perform poorly in FOPEN SAR images. Using symmetric alpha-stable (SαS) densities to model the impulsive noise, we have developed a region-adaptive automatic target detection (ATD) algorithm. The image is first segmented, and the resulting labeled image is exploited by a region-adaptive target detection algorithm. In this paper, we evaluate the performance of our algorithm in different frequency bands, and determine which subbands are useful for image segmentation and target detection.
| Original language | English (US) |
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| Pages | 578-582 |
| Number of pages | 5 |
| State | Published - 1998 |
| Externally published | Yes |
| Event | Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) - Chicago, IL, USA Duration: Oct 4 1998 → Oct 7 1998 |
Other
| Other | Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) |
|---|---|
| City | Chicago, IL, USA |
| Period | 10/4/98 → 10/7/98 |
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering