Use of Sentinel-1 Dual Polarization Multi-Temporal Data with Gray Level Co-Occurrence Matrix Textural Parameters for Building Damage Assessment

Akhmadiya, Asset; Nabiyev, Nabi; Moldamurat, Khuralay; Dyussekeyev, Kanagat; Atanov, Sabyrzhan

doi:10.1134/s1054661821020036

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…There are a large number of different sensors available for making measurements [56], [57]. Sensors are widely used in various fields, such as scientific research, testing, quality control, automated control systems, and others [58]- [60].…”

Section: B Development Of a Prototype Gas Analyzer For Data Collectionmentioning

confidence: 99%

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Mukanova,

Atanov,

Jamshidi

2023

Journal of Robotics and Control

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The constant emission of polluting gases is causing an urgent need for timely detection of harmful gas mixtures in the atmosphere. A method and algorithm of the determining spectral composition of gas with a gas analyzer using an artificial neural network (ANN) were suggested in the article. A small closed gas dynamic system was designed and used as an experimental bench for collecting and quantifying gas concentrations for testing the proposed method. This device was based on AS7265x and BMP180 sensors connected in parallel to a 3.3 V compatible Arduino Uno board via QWIIC. Experimental tests were conducted with air from the laboratory room, carbon dioxide (CO2), and a mixture of pure oxygen (O2) with nitrogen (N2) in a 9:1 ratio. Three ANNs with one input, one hidden and one output layer were built. The ANN had 5, 10, and 20 hidden neurons, respectively. The dataset was divided into three parts: 70% for training, 15% for validation, and 15% for testing. The mean square error (MSE) error and regression were analyzed during training. Training, testing, and validation error analysis were performed to find the optimal iteration, and the MSE versus training iteration was plotted. The best indicators of training and construction were shown by the ANN with 5 (five) hidden layers, and 16 iterations are enough to train, test and verify this neural network. To test the obtained neural network, the program code was written in the MATLAB. The proposed scheme of the gas analyzer is operable and has a high accuracy of gas detection with a given error of 3%. The results of the study can be used in the development of an industrial gas analyzer for the detection of harmful gas mixtures.

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Section: B Development Of a Prototype Gas Analyzer For Data Collectionmentioning

confidence: 99%

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

Mukanova,

Atanov,

Jamshidi

2023

Journal of Robotics and Control

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“…In spite of this, normal urban evolution may also produce abundant changes in addition to earthquake impact. Furthermore, the lack of pre-earthquake data after earthquakes is often the bottleneck that restricts the popularization and application of such methods [10][11][12]. (2) Methods based on post-earthquake images: Such methods eliminate the dependence on pre-earthquake data and have stronger universality compared with methods based on pre-earthquake and post-earthquake images [13].…”

Section: Introductionmentioning

confidence: 99%

A Detection Method for Collapsed Buildings Combining Post-Earthquake High-Resolution Optical and Synthetic Aperture Radar Images

et al. 2022

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The detection of collapsed buildings based on post-earthquake remote sensing images is conducive to eliminating the dependence on pre-earthquake data, which is of great significance to carry out emergency response in time. The difficulties in obtaining or lack of elevation information, as strong evidence to determine whether buildings collapse or not, is the main challenge in the practical application of this method. On the one hand, the introduction of double bounce features in synthetic aperture radar (SAR) images are helpful to judge whether buildings collapse or not. On the other hand, because SAR images are limited by imaging mechanisms, it is necessary to introduce spatial details in optical images as supplements in the detection of collapsed buildings. Therefore, a detection method for collapsed buildings combining post-earthquake high-resolution optical and SAR images was proposed by mining complementary information between traditional visual features and double bounce features from multi-source data. In this method, a strategy of optical and SAR object set extraction based on an inscribed center (OpticalandSAR-ObjectsExtraction) was first put forward to extract a unified optical-SAR object set. Based on this, a quantitative representation of collapse semantic knowledge in double bounce (DoubleBounceCollapseSemantic) was designed to bridge a semantic gap between double bounce and collapse features of buildings. Ultimately, the final detection results were obtained based on the improved active learning support vector machines (SVMs). The multi-group experimental results of post-earthquake multi-source images show that the overall accuracy (OA) and the detection accuracy for collapsed buildings (Pcb) of the proposed method can reach more than 82.39% and 75.47%. Therefore, the proposed method is significantly superior to many advanced methods for comparison.

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