A reconfigurable multi-mode implementation of hyperspectral target detection algorithms

2020 ◽  
Vol 78 ◽  
pp. 103258
Author(s):  
Đorđije Bošković ◽  
Milica Orlandić ◽  
Tor Arne Johansen
Keyword(s):  
Target Detection ◽  
Detection Algorithms ◽  
Multi Mode

scholarly journals Angle Distance-Based Hierarchical Background Separation Method for Hyperspectral Imagery Target Detection

2020 ◽  
Vol 12 (4) ◽  
pp. 697 ◽  
Author(s):  
Xiaohui Hao ◽  
Yiquan Wu ◽  
Peng Wang
Keyword(s):  
Target Detection ◽  
Hyperspectral Imagery ◽  
Original Data ◽  
Separation Method ◽  
Detection Accuracy ◽  
Target Information ◽  
Detection Algorithms ◽  
Target Spectrum ◽  
Candidate Target ◽  
The Given

Traditional detectors for hyperspectral imagery (HSI) target detection (TD) output the result after processing the HSI only once. However, using the prior target information only once is not sufficient, as it causes the inaccuracy of target extraction or the unclean separation of the background. In this paper, the target pixels are located by a hierarchical background separation method, which explores the relationship between the target and the background for making better use of the prior target information more than one time. In each layer, there is an angle distance (AD) between each pixel spectrum in HSI and the given prior target spectrum. The AD between the prior target spectrum and candidate target ones is smaller than that of the background pixels. The AD metric is utilized to adjust the values of pixels in each layer to gradually increase the separability of the background and the target. For making better discrimination, the AD is calculated through the whitened data rather than the original data. Besides, an elegant and ingenious smoothing processing operation is employed to mitigate the influence of spectral variability, which is beneficial for the detection accuracy. The experimental results of three real hyperspectral images show that the proposed method outperforms other classical and recently proposed HSI target detection algorithms.

scholarly journals A Comparative Study of Moving Target Detection Algorithms

2020 ◽  
Vol 790 ◽  
pp. 012061
Author(s):  
Yuanyuan Wang ◽  
Xiaolei Zhou ◽  
Yuanyuan Zuo ◽  
Zhuang Wu
Keyword(s):  
Comparative Study ◽  
Target Detection ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection Algorithms

Application of target detection algorithms to identification of iron oxides using ASTER images: a case study in the North of Semnan province, Iran

2014 ◽  
Vol 8 (9) ◽  
pp. 7321-7331 ◽  
Author(s):  
Majid Rahimzadegan ◽  
Behnam Sadeghi ◽  
Manuchehr Masoumi ◽  
Siamak Taghizadeh Ghalehjoghi
Keyword(s):  
Iron Oxides ◽  
Target Detection ◽  
Detection Algorithms ◽  
The North ◽  
Semnan Province
Sign in / Sign up

Export Citation Format

Share Document