Feature extraction of attributed scattering centers on high resolution SAR imagery

2007 ◽  
Author(s):  
Jin Yang ◽  
Dong-mei Yan ◽  
Chao Wang ◽  
Hong Zhang
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Scattering Centers ◽  
Sar Imagery

Feature Extraction of Scattering Centers in High-resolution SAR Image

2011 ◽  
Vol 33 (7) ◽  
pp. 1661-1666 ◽  
Author(s):  
Jun Lou ◽  
Tian Jin ◽  
Qian Song ◽  
Zhi-min Zhou
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Sar Image ◽  
Scattering Centers

Feature Extraction of Ground Vehicles from High Resolution SAR Imagery Based on Electromagnetic Scattering Characteristic

2012 ◽  
Vol 190-191 ◽  
pp. 778-785
Author(s):  
Ke Feng Ji ◽  
Xiang Wei Xing ◽  
Jing Ke Zhang ◽  
Huan Xin Zhou
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Electromagnetic Scattering ◽  
Target Recognition ◽  
Ground Vehicles ◽  
Scattering Mechanism ◽  
Modified Method ◽  
Scattering Center ◽  
Sar Imagery ◽  
Scattering Characteristic

Feature extraction is an important phase in SAR imagery interpretation and target recognition, electromagnetic scattering characteristic is the inherent characteristic of target on SAR imagery. High resolution SAR imagery interpretation and target recognition, particularly the ground vehicles recognition, needs to be carried through based on target scattering characteristic and target’s electromagnetic feature extraction. This paper analyzes the electromagnetic scattering mechanism of typical ground vehicles firstly, and the feasibility of modeling the electromagnetic scattering characteristic of ground vehicles with attributed scattering center model has been discussed in brief. And then, by making some improvement on the existing method of attributed scattering center feature extraction from SAR imagery, a modified method for feature extraction of ground vehicles from measured high resolution SAR imagery based on electromagnetic scattering characteristic has been proposed. Finally, performance of the proposed algorithm has been validated on measured 0.3m resolution MSTAR and 0.1m resolution MiniSAR SAR imagery data.

Auto-interpretation for Bridges over Water in High-resolution Space-borne SAR Imagery

2011 ◽  
Vol 33 (7) ◽  
pp. 1706-1712 ◽  
Author(s):  
Shao-ming Zhang ◽  
Xiang-chen He ◽  
Xiao-hu Zhang ◽  
Yi-wei Sun
Keyword(s):  
High Resolution ◽  
Sar Imagery

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

RPCA-Based High Resolution Through-the-Wall Human Motion Feature Extraction and Classification

2021 ◽  
pp. 1-1
Author(s):  
Qiang An ◽  
Shuoguang Wang ◽  
Lei Yao ◽  
Wenji Zhang ◽  
Hao Lv ◽  
...  
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Human Motion ◽  
Motion Feature

scholarly journals Comparison of high-resolution wind fields extracted from TerraSAR-X SAR imagery with predictions from the WRF mesoscale model

2012 ◽  
Vol 117 (C2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Donald R. Thompson ◽  
Jochen Horstmann ◽  
Alexis Mouche ◽  
Nathaniel S. Winstead ◽  
Raymond Sterner ◽  
...  
Keyword(s):  
High Resolution ◽  
Mesoscale Model ◽  
Wind Fields ◽  
Sar Imagery
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