Performance analysis of different predictive models for crop classification across an aridic to ustic area of Indian states

Shukla, Gaurav; Garg, Rahul; Srivastava, H. S.; Garg, Pradeep

doi:10.1080/10106049.2016.1240721

Cited by 19 publications

(8 citation statements)

References 44 publications

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“…The height of ambiguity can be written as (3). According to (3), the interferometric phase obtained by backprojection InSAR elevation inversion model corresponds to the elevation error of the external DEM in BP imaging, and the terrain phase has been removed.…”

Section: A Backprojection Insar Elevation Inversion Modelmentioning

confidence: 99%

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A Novel Airborne Dual-Antenna InSAR Calibration Method for Backprojection Imaging Model

Wang

Xiang

et al. 2021

IEEE Access

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The interferometric synthetic aperture radar (InSAR) elevation inversion method based on the backprojection (BP) imaging model simplifies the interferometric processing chain where image registration and phase unwrapping are not required. Meanwhile, BP algorithm is suitable for radar with different imaging modes and geometry. However, the existing calibration methods are all aimed at traditional InSAR which adopts frequency domain imaging algorithm, but the calibration method for backprojection InSAR elevation inversion model has not been published. In this paper, we propose a novel InSAR calibration method which combines Fast Fourier Transform (FFT) estimation method with sensitivity equation method based on backprojection imaging model. FFT is used to process the interferometric phase which has removed the terrain phase of external digital elevation model (DEM) under BP imaging, and the estimation of interferometric parameters is realized through the phase fringe frequency. In the calibration method based on sensitivity equations, the deviations of interferometric parameters are calculated by solving sensitivity equations according to the three-dimensional information of ground control points. FFT estimation method not only generates the initial baseline parameters for sensitivity equation method, which is conducive to obtaining the global optimal solution, but also separates the calibration of phase offset and baseline parameters, which makes it possible to minimize the condition number of sensitivity matrix in sensitivity equation method. Finally, we obtain the calibrated interferometric parameters through iteration of sensitivity equation method and realize high accuracy DEM inversion. Both simulation experiment and airborne dualantenna InSAR data processing are performed to verify the effectiveness of this method.INDEX TERMS backprojection imaging model, FFT baseline estimation, InSAR calibration, sensitivity equations.

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Section: A Backprojection Insar Elevation Inversion Modelmentioning

confidence: 99%

“…Digital elevation model (DEM), which depicts topographic relief, has been widely used in topographic mapping, hydrology, and agriculture [1]- [3]. Interferometric synthetic aperture radar (InSAR) technology is an important approach to obtain DEM, which has the advantages of all weather, high efficiency, and good accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Airborne Dual-Antenna InSAR Calibration Method for Backprojection Imaging Model

Wang

Xiang

et al. 2021

IEEE Access

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“…Conservation agriculture has long been viewed as a cost-effective and environmentally friendly method of increasing crop yield (Xiao et al, 2020). Shukla (2018) in her research compares the performance of various machine learning methods for large-scale crop classification. It is found from the literature review that the crops like Wheat, paddy, red tensils, etc were used in most of the places in the southern districts of Tamilnadu.…”

Section: Introductionmentioning

confidence: 99%

A Study on Usage of Agricultural Engineering Equipment for Various Crops and Yields in South Tamilnadu

Chandrakumar¹,

Sakthipriya²,

S.³

2023

International Journal of Social Ecology and Sustainable Development

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Agriculture is one of the unique fields to contribute a major part of Indian economic growth. Engineering machinery plays a vital role in the farming culture in India. The focal point of this research is fully concentrated with Agri engineering equipment and crop yield based on the repository taken from various sources like the internet, agri departments, government agencies, etc. The research study shows statistical proof of crop production and machinery used for farming in the south zone of India. In particular, southern components of Tamilnadu state are taken as a sample for this research. It has been identified paddy is widely cropped in the majority of the districts like Madurai, Theni, Dindigul, etc. Machine learning techniques are now being used in the Agri sectors for the prediction of crop yields and as a result visualization of data done in this research for the various crops and machinery used. It is to conclude that Agri machinery is nowadays used for farming for better yield of the crops.

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“…DEMs are the fundamental tools of geo-analysis and the essential input variables for many models. They have been widely used in science and engineering fields such as water resource management [1][2][3], agriculture [4,5], and ecology [6][7][8]. DEMs can be derived not only from scanned/vectorized contour lines of existing topographic sheets obtained by field measurement [9], but also from remote sensing techniques.…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Open Source Digital Elevation Models with Multi-Scale Fusion and a Slope Position-Based Linear Regression Method

et al. 2018

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The growing need to monitor changes in the surface of the Earth requires a high-quality, accessible Digital Elevation Model (DEM) dataset, whose development has become a challenge in the field of Earth-related research. The purpose of this paper is to improve the overall accuracy of public domain DEMs by data fusion. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods—the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD), and nonlinear adaptive multi-scale decomposition (N-AMD)—are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D—30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the Ice Cloud and Land Elevation Satellite (ICESat) global land surface altimetry data as a reference. The fused DEMs offer significant improvements of 29.6% and 19.3% in RMSE at a mountainous site, and 27.4% and 15.5% over a low-relief region, compared to the SRTM-1 and AW3D30, respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region, respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.

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Performance analysis of different predictive models for crop classification across an aridic to ustic area of Indian states

Cited by 19 publications

References 44 publications

A Novel Airborne Dual-Antenna InSAR Calibration Method for Backprojection Imaging Model

A Novel Airborne Dual-Antenna InSAR Calibration Method for Backprojection Imaging Model

A Study on Usage of Agricultural Engineering Equipment for Various Crops and Yields in South Tamilnadu

Improving the Accuracy of Open Source Digital Elevation Models with Multi-Scale Fusion and a Slope Position-Based Linear Regression Method

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