Hyperspectral Image Target Detection Improvement Based on Total Variation

2016 ◽  
Vol 25 (5) ◽  
pp. 2249-2258 ◽  
Author(s):  
Shuo Yang ◽  
Zhenwei Shi
Keyword(s):  
Total Variation ◽  
Target Detection ◽  
Hyperspectral Image

scholarly journals Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification

2021 ◽  
Vol 13 (6) ◽  
pp. 1143
Author(s):  
Yinghui Quan ◽  
Yingping Tong ◽  
Wei Feng ◽  
Gabriel Dauphin ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Total Variation ◽  
Structure Analysis ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Heterogeneous Data ◽  
Lidar Data ◽  
Fusion Method ◽  
Wide Range ◽  
Relative Total Variation

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.

scholarly journals Hyperspectral Image Reconstruction via Block Low-Rank and Three-Dimension Weighted Total Variation Constraint

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47698-47713 ◽  
Author(s):  
Zongrui Wu ◽  
Xi Chen ◽  
Wenxuan Shi ◽  
Liqiong Chen ◽  
Shiyong Hu
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Hyperspectral Image ◽  
Low Rank ◽  
Three Dimension ◽  
Weighted Total Variation

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

scholarly journals Structure Extraction With Total Variation for Hyperspectral Image Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 91019-91033
Author(s):  
Qiaoqiao Li ◽  
Haibo Wang ◽  
Guoyue Chen ◽  
Kazuki Saruta ◽  
Yuki Terata
Keyword(s):  
Image Classification ◽  
Total Variation ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Structure Extraction

Target Detection of Hyperspectral Image Based on Faster R-CNN with Data Set Adjustment and Parameter Turning

2019 ◽  
Author(s):  
Xuefeng Liu ◽  
Congcong Wang ◽  
Hao Wang ◽  
Min Fu ◽  
Yuping Feng ◽  
...  
Keyword(s):  
Target Detection ◽  
Hyperspectral Image ◽  
Data Set
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