Deep Latent Spectral Representation Learning-Based Hyperspectral Band Selection for Target Detection

2020 ◽  
Vol 58 (3) ◽  
pp. 2015-2026
Author(s):  
Weiying Xie ◽  
Jie Lei ◽  
Jian Yang ◽  
Yunsong Li ◽  
Qian Du ◽  
...  
Keyword(s):  
Target Detection ◽  
Spectral Representation ◽  
Representation Learning ◽  
Band Selection ◽  
Selection For

Target-Constrained Interference-Minimized Band Selection for Hyperspectral Target Detection

2020 ◽  
pp. 1-21
Author(s):  
Xiaodi Shang ◽  
Meiping Song ◽  
Yulei Wang ◽  
Chunyan Yu ◽  
Haoyang Yu ◽  
...  
Keyword(s):  
Target Detection ◽  
Band Selection ◽  
Selection For

scholarly journals A BAND SELECTION METHOD FOR SUB-PIXEL TARGET DETECTION IN HYPERSPECTRAL IMAGES BASED ON LABORATORY AND FIELD REFLECTANCE SPECTRAL COMPARISON

2016 ◽  
Vol XLI-B7 ◽  
pp. 117-120
Author(s):  
S. Sharifi hashjin ◽  
A. Darvishi ◽  
S. Khazai ◽  
F. Hatami ◽  
M. Jafari houtki
Keyword(s):  
Target Detection ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Band Selection ◽  
Blind Test ◽  
Simple Method ◽  
Test Dataset ◽  
Detection Algorithms ◽  
Selection For ◽  
Field Reflectance

In recent years, developing target detection algorithms has received growing interest in hyperspectral images. In comparison to the classification field, few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. This study presents a simple method to remove bad bands from the images in a supervised manner for sub-pixel target detection. The proposed method is based on comparing field and laboratory spectra of the target of interest for detecting bad bands. For evaluation, the target detection blind test dataset is used in this study. Experimental results show that the proposed method can improve efficiency of the two well-known target detection methods, ACE and CEM.

Band Selection for Specific Target Detection of Hyperspectral Imagery

2021 ◽  
Author(s):  
Xudong Sun ◽  
Site Li ◽  
Hongqi Zhang ◽  
Fengqiang Xu ◽  
Xianping Fu
Keyword(s):  
Target Detection ◽  
Hyperspectral Imagery ◽  
Band Selection ◽  
Specific Target ◽  
Selection For

scholarly journals A BAND SELECTION METHOD FOR SUB-PIXEL TARGET DETECTION IN HYPERSPECTRAL IMAGES BASED ON LABORATORY AND FIELD REFLECTANCE SPECTRAL COMPARISON

2016 ◽  
Vol XLI-B7 ◽  
pp. 117-120
Author(s):  
S. Sharifi hashjin ◽  
A. Darvishi ◽  
S. Khazai ◽  
F. Hatami ◽  
M. Jafari houtki
Keyword(s):  
Target Detection ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Band Selection ◽  
Blind Test ◽  
Simple Method ◽  
Test Dataset ◽  
Detection Algorithms ◽  
Selection For ◽  
Field Reflectance

In recent years, developing target detection algorithms has received growing interest in hyperspectral images. In comparison to the classification field, few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. This study presents a simple method to remove bad bands from the images in a supervised manner for sub-pixel target detection. The proposed method is based on comparing field and laboratory spectra of the target of interest for detecting bad bands. For evaluation, the target detection blind test dataset is used in this study. Experimental results show that the proposed method can improve efficiency of the two well-known target detection methods, ACE and CEM.

Constrained-Target Band Selection for Multiple-Target Detection

2019 ◽  
Vol 57 (8) ◽  
pp. 6079-6103 ◽  
Author(s):  
Yulei Wang ◽  
Lin Wang ◽  
Chunyan Yu ◽  
Enyu Zhao ◽  
Meiping Song ◽  
...  
Keyword(s):  
Target Detection ◽  
Band Selection ◽  
Multiple Target ◽  
Selection For

scholarly journals Joint Skewness and Its Application in Unsupervised Band Selection for Small Target Detection

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Xiurui Geng ◽  
Kang Sun ◽  
Luyan Ji ◽  
Hairong Tang ◽  
Yongchao Zhao
Keyword(s):  
Target Detection ◽  
Band Selection ◽  
Small Target ◽  
Small Target Detection ◽  
Selection For
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