scholarly journals Frequency and Polarization-Diversified Linear Sampling Methods for Microwave Tomography and Remote Sensing Using Electromagnetic Metamaterials

Electronics ◽  
2017 ◽  
Vol 6 (4) ◽  
pp. 85 ◽  
Author(s):  
Mehdi Salarkaleji ◽  
Mohammadreza Eskandari ◽  
Jimmy Chen ◽  
Chung-Tse Wu
Keyword(s):  
Remote Sensing ◽  
Three Dimensional ◽  
Shape Reconstruction ◽  
Space Mapping ◽  
Single Frequency ◽  
Microwave Tomography ◽  
Frequency Space ◽  
Frequency Scanning ◽  
Frequency Scanning Antennas ◽  
Linear Sampling

Metamaterial leaky wave antennas (MTM-LWAs), one kind of frequency scanning antennas, exhibit frequency-space mapping characteristics that can be utilized to obtain a sufficient field of view (FOV) and reconstruct shapes in both remote sensing and microwave imaging. In this article, we utilize MTM-LWAs to conduct a spectrally encoded three-dimensional (3D) microwave tomography and remote sensing that can reconstruct conductive targets with various dimensions. In this novel imaging technique, we employ the linear sampling method (LSM) as a powerful and fast reconstruction approach. Unlike the traditional LSM using only one single frequency to illuminate a fixed direction, the proposed method utilizes a frequency scanning MTM antenna array able to accomplish frequency-space mapping over the targeted 3D background that includes unknown objects. In addition, a novel technique based on a frequency and polarization hybrid method is proposed to improve the shape reconstruction resolution and stability in ill-posed inverse problems. Both simulation and experimental results demonstrate the unique advantages of the proposed LSM using MTM-LWAs with frequency and polarization diversity as an efficient 3D remote sensing and tomography scheme.

scholarly journals Correction: Salarkaleji, M. Frequency and Polarization-Diversified Linear Sampling Methods for Microwave Tomography and Remote Sensing Using Electromagnetic Metamaterials. Electronics 2017, 6, 85

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 415
Author(s):  
Mehdi Salarkaleji ◽  
Mohammadreza Eskandari ◽  
Jimmy Ching-Ming Chen ◽  
Chung-Tse Michael Wu
Keyword(s):  
Remote Sensing ◽  
Sampling Methods ◽  
Original Version ◽  
Microwave Tomography ◽  
Electromagnetic Metamaterials ◽  
Linear Sampling

The authors would like to change the affiliation for second author, Mohammadreza Eskandari, as listed in the original version of the article [...]

scholarly journals Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 951-960
Author(s):  
Haiqing Zhang ◽  
Jun Han
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Specific Class ◽  
Three Dimensional Model ◽  
Image Information ◽  
Information Classification ◽  
Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

scholarly journals The developement of red mud flood environmental information system and the methodology for the spatial analysis of the degraded area

2015 ◽  
Vol 9 (1) ◽  
pp. 1-11
Author(s):  
Gábor Bakó ◽  
Gábor Kovács ◽  
Zsolt Molnár ◽  
Judit Kirisics ◽  
Eszter Góber ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Red Mud ◽  
High Spatial Resolution ◽  
Three Dimensional ◽  
Environmental Information ◽  
Evaluation Methodology ◽  
Aerial Photogrammetry ◽  
Impact Area ◽  
The Impact

The red mud disaster occurred on 4th October 2010 in Hungary has raised the necessity of rapid intervention and drew attention to the long-term monitoring of such threat. Both the condition assessment and the change monitoring indispensably required the prompt and detailed spatial survey of the impact area. It was conducted by several research groups - independently - with different recent surveying methods. The high spatial resolution multispectral aerial photogrammetry is the spatially detailed (high resolution) and accurate type of remote sensing. The hyperspectral remote sensing provides more information about material quality of pollutants, with less spatial details and lower spatial accuracy, while LIDAR ensures the three-dimensional shape and terrain models. The article focuses on the high spatial resolution, multispectral electrooptical method and the evaluation methodology of the deriving high spatial resolution ortho image map, presenting the derived environmental information database

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