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 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.

Synthetic sampling for spatio-temporal land cover mapping with machine learning and the Google Earth Engine in Andalusia, Spain

2020 ◽  
Author(s):  
Laura Bindereif ◽  
Tobias Rentschler ◽  
Martin Batelheim ◽  
Marta Díaz-Zorita Bonilla ◽  
Philipp Gries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Classification Accuracy ◽  
Sampling Methods ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Machine Learning Applications ◽  
Google Earth Engine ◽  
Spatio Temporal

<p>Land cover information plays an essential role for resource development, environmental monitoring and protection. Amongst other natural resources, soils and soil properties are strongly affected by land cover and land cover change, which can lead to soil degradation. Remote sensing techniques are very suitable for spatio-temporal mapping of land cover mapping and change detection. With remote sensing programs vast data archives were established. Machine learning applications provide appropriate algorithms to analyse such amounts of data efficiently and with accurate results. However, machine learning methods require specific sampling techniques and are usually made for balanced datasets with an even training sample frequency. Though, most real-world datasets are imbalanced and methods to reduce the imbalance of datasets with synthetic sampling are required. Synthetic sampling methods increase the number of samples in the minority class and/or decrease the number in the majority class to achieve higher model accuracy. The Synthetic Minority Over-Sampling Technique (SMOTE) is a method to generate synthetic samples and balance the dataset used in many machine learning applications. In the middle Guadalquivir basin, Andalusia, Spain, we used random forests with Landsat images from 1984 to 2018 as covariates to map the land cover change with the Google Earth Engine. The sampling design was based on stratified random sampling according to the CORINE land cover classification of 2012. The land cover classes in our study were arable land, permanent crops (plantations), pastures/grassland, forest and shrub. Artificial surfaces and water bodies were excluded from modelling. However, the number of the 130 training samples was imbalanced. The classes pasture (7 samples) and shrub (13 samples) show a lower number than the other classes (48, 47 and 16 samples). This led to misclassifications and negatively affected the classification accuracy. Therefore, we applied SMOTE to increase the number of samples and the classification accuracy of the model. Preliminary results are promising and show an increase of the classification accuracy, especially the accuracy of the previously underrepresented classes pasture and shrub. This corresponds to the results of studies with other objectives which also see the use of synthetic sampling methods as an improvement for the performance of classification frameworks.</p>

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