Next Article in Journal
Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data
Previous Article in Journal
Detection of Rice Phenological Variations under Heavy Metal Stress by Means of Blended Landsat and MODIS Image Time Series
Open AccessArticle

Visual Detail Augmented Mapping for Small Aerial Target Detection

1
School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
2
SAIIP School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 14; https://doi.org/10.3390/rs11010014
Received: 23 October 2018 / Revised: 4 December 2018 / Accepted: 19 December 2018 / Published: 21 December 2018
Moving target detection plays a primary and pivotal role in avionics visual analysis, which aims to completely and accurately detect moving objects from complex backgrounds. However, due to the relatively small sizes of targets in aerial video, many deep networks that achieve success in normal size object detection are usually accompanied by a high rate of false alarms and missed detections. To address this problem, we propose a novel visual detail augmented mapping approach for small aerial target detection. Concretely, we first present a multi-cue foreground segmentation algorithm including motion and grayscale information to extract potential regions. Then, based on the visual detail augmented mapping approach, the regions that might contain moving targets are magnified to multi-resolution to obtain detailed target information and rearranged into new foreground space for visual enhancement. Thus, original small targets are mapped to a more efficient foreground augmented map which is favorable for accurate detection. Finally, driven by the success of deep detection network, small moving targets can be well detected from aerial video. Experiments extensively demonstrate that the proposed method achieves success in small aerial target detection without changing the structure of the deep network. In addition, compared with the-state-of-art object detection algorithms, it performs favorably with high efficiency and robustness. View Full-Text
Keywords: small target detection; aerial video; visual detail augmented mapping small target detection; aerial video; visual detail augmented mapping
Show Figures

Figure 1

MDPI and ACS Style

Li, J.; Dai, Y.; Li, C.; Shu, J.; Li, D.; Yang, T.; Lu, Z. Visual Detail Augmented Mapping for Small Aerial Target Detection. Remote Sens. 2019, 11, 14.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop