Underwater target detection with hyperspectral imagery for search and rescue missions

2018 ◽  
Author(s):  
İsa Cem Eken ◽  
Yasemin Yardimci Çetin
Keyword(s):  
Target Detection ◽  
Hyperspectral Imagery ◽  
Search And Rescue ◽  
Underwater Target Detection ◽  
Underwater Target

scholarly journals Underwater Hyperspectral Target Detection with Band Selection

2020 ◽  
Vol 12 (7) ◽  
pp. 1056 ◽  
Author(s):  
Xianping Fu ◽  
Xiaodi Shang ◽  
Xudong Sun ◽  
Haoyang Yu ◽  
Meiping Song ◽  
...  
Keyword(s):  
Target Detection ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Minimum Variance ◽  
High Spectral Resolution ◽  
Band Selection ◽  
Protective Mechanism ◽  
Underwater Target Detection ◽  
Underwater Targets ◽  
Underwater Target

Compared to multi-spectral imagery, hyperspectral imagery has very high spectral resolution with abundant spectral information. In underwater target detection, hyperspectral technology can be advantageous in the sense of a poor underwater imaging environment, complex background, or protective mechanism of aquatic organisms. Due to high data redundancy, slow imaging speed, and long processing of hyperspectral imagery, a direct use of hyperspectral images in detecting targets cannot meet the needs of rapid detection of underwater targets. To resolve this issue, a fast, hyperspectral underwater target detection approach using band selection (BS) is proposed. It first develops a constrained-target optimal index factor (OIF) band selection (CTOIFBS) to select a band subset with spectral wavelengths specifically responding to the targets of interest. Then, an underwater spectral imaging system integrated with the best-selected band subset is constructed for underwater target image acquisition. Finally, a constrained energy minimization (CEM) target detection algorithm is used to detect the desired underwater targets. Experimental results demonstrate that the band subset selected by CTOIFBS is more effective in detecting underwater targets compared to the other three existing BS methods, uniform band selection (UBS), minimum variance band priority (MinV-BP), and minimum variance band priority with OIF (MinV-BP-OIF). In addition, the results also show that the acquisition and detection speed of the designed underwater spectral acquisition system using CTOIFBS can be significantly improved over the original underwater hyperspectral image system without BS.

scholarly journals A Self-Improving Framework for Joint Depth Estimation and Underwater Target Detection from Hyperspectral Imagery

2021 ◽  
Vol 13 (9) ◽  
pp. 1721
Author(s):  
Jiahao Qi ◽  
Pengcheng Wan ◽  
Zhiqiang Gong ◽  
Wei Xue ◽  
Aihuan Yao ◽  
...  
Keyword(s):  
Target Detection ◽  
Structural Information ◽  
Hyperspectral Imagery ◽  
Contextual Information ◽  
Water Environment ◽  
Depth Estimation ◽  
Detection Methods ◽  
Depth Information ◽  
Underwater Target Detection ◽  
Underwater Target

Underwater target detection (UTD) is one of the most attractive research topics in hyperspectral imagery (HSI) processing. Most of the existing methods are presented to predict the signatures of desired targets in an underwater context but ignore the depth information which is position-sensitive and contributes significantly to distinguishing the background and target pixels. So as to take full advantage of the depth information, in this paper a self-improving framework is proposed to perform joint depth estimation and underwater target detection, which exploits the depth information and detection results to alternately boost the final detection performance. However, it is difficult to calculate depth information under the interference of a water environment. To address this dilemma, the proposed framework, named self-improving underwater target detection framework (SUTDF), employs the spectral and spatial contextual information to pick out target-associated pixels as the guidance dataset for depth estimation work. Considering the incompleteness of the guidance dataset, an expectation-maximum liked updating scheme has also been developed to iteratively excavate the statistical and structural information from input HSI for further improving the diversity of the guidance dataset. During each updating epoch, the calculated depth information is used to yield a more diversified dataset for the target detection network, leading to a more accurate detection result. Meanwhile, the detection result will in turn contribute in detecting more target-associated pixels as the supplement for the guidance dataset, eventually promoting the capacity of the depth estimation network. With this specific self-improving framework, we can provide a more precise detection result for a hyperspectral UTD task. Qualitative and quantitative illustrations verify the effectiveness and efficiency of SUTDF in comparison with state-of-the-art underwater target detection methods.

Lidar-radar for underwater target detection using a modulated sub-nanosecond Q-switched laser

2021 ◽  
Vol 142 ◽  
pp. 107234
Author(s):  
Guangying Li ◽  
Qiang Zhou ◽  
Guoquan Xu ◽  
Xing Wang ◽  
Wenjie Han ◽  
...  
Keyword(s):  
Target Detection ◽  
Underwater Target Detection ◽  
Underwater Target
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