An Unsupervised Band Selection Based on Band Similarity for Hyperspectral Image Target Detection

2014 ◽  
Author(s):  
Yan Cao ◽  
Jing Zhang ◽  
Li Zhuo ◽  
Chao Wang ◽  
Qianlan Zhou
Keyword(s):  
Target Detection ◽  
Hyperspectral Image ◽  
Band Selection

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 Bathymetric-Based Band Selection Method for Hyperspectral Underwater Target Detection

2021 ◽  
Vol 13 (19) ◽  
pp. 3798
Author(s):  
Jiahao Qi ◽  
Zhiqiang Gong ◽  
Aihuan Yao ◽  
Xingyue Liu ◽  
Yongqian Li ◽  
...  
Keyword(s):  
Target Detection ◽  
Hyperspectral Image ◽  
Selection Process ◽  
Representation Learning ◽  
Selection Method ◽  
Band Selection ◽  
Hyperspectral Image Processing ◽  
Effectiveness And Efficiency ◽  
Underwater Target Detection ◽  
Underwater Target

Band selection has imposed great impacts on hyperspectral image processing in recent years. Unfortunately, few existing methods are proposed for hyperspectral underwater target detection (HUTD). In this paper, a novel unsupervised band selection method is proposed for HUTD by embedding the bathymetric model into the band selection process. Considering the dependence between targets and background, a bathymetric latent spectral representation learning scheme is designed to investigate a physically meaningful subspace where the desired targets are the most distinguishable from the background. This calculated subspace is exploited as a reference to select out desired bands based on the spectral distance metric. Then, we propose an iteration-based band subset generation strategy for the sake of promoting the diversity of the band selection results and taking full advantage of the ample spectral information. Moreover, a representative band selection approach based on sparse representation is also conducted to eliminate the redundant information among adjacent bands. The band selection result is eventually achievable by connecting the representative bands of all the band subsets. Qualitative and quantitative evaluations demonstrate the effectiveness and efficiency of the proposed method in comparison with state-of-the-art band selection methods.

A Fast Volume-Gradient-Based Band Selection Method for Hyperspectral Image

2014 ◽  
Vol 52 (11) ◽  
pp. 7111-7119 ◽  
Author(s):  
Xiurui Geng ◽  
Kang Sun ◽  
Luyan Ji ◽  
Yongchao Zhao
Keyword(s):  
Hyperspectral Image ◽  
Selection Method ◽  
Band Selection ◽  
Gradient Based

Self-spectral learning with GAN based spectral-spatial target detection for hyperspectral image

2021 ◽  
Author(s):  
Weiying Xie ◽  
Jiaqing Zhang ◽  
Jie Lei ◽  
Yunsong Li ◽  
Xiuping Jia
Keyword(s):  
Target Detection ◽  
Hyperspectral Image ◽  
Spectral Learning
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