Using Experimental Models and Multitemporal Landsat-9 Images for Cadmium Concentration Mapping

Felegari, Shilan; Sharifi, Alireza; Khosravi, Mohammad Farough; Sabanov, Sergei

doi:10.1109/lgrs.2023.3291019

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…(30) 𝑥 𝑘 = 𝐹𝑢𝑠𝑖𝑜𝑛 (𝑥 𝑘 (1) , 𝑥 𝑘 (2) The OWA fusion method is a general form of examined min, max, mean and median methods. The OWA performance in the considered problem can be represented as follows:…”

Section: Comparison Of Ultra-fusion With Other Fusion Methodsmentioning

confidence: 99%

“…(31) 𝑥 𝑘 = 𝐹𝑢𝑠𝑖𝑜𝑛 (𝑥 𝑘 (1) , 𝑥 𝑘 (2) ) = 𝑤𝑥 𝑘 (1) + (1 − 𝑤)𝑥 𝑘 (2) , 0 ≤ 𝑤 Indeed, the OWA checks the 𝐶𝐹 value for all allowed 𝑤 values (0 ≤ 𝑤 ≤ 1) and selects the best 𝑤 value, where the 𝐶𝐹 will be optimized. This concept is equivalent to checking all points on the main diagonal of input space rectangle, point by point, and selecting the best one (as illustrated in Fig.…”

Section: Comparison Of Ultra-fusion With Other Fusion Methodsmentioning

confidence: 99%

“…This method is the same as the OWA method with this difference that an additive white Gaussian noise has been added. The corresponding relation for this method is as follows: (33) 𝑥 𝑘 = 𝐹𝑢𝑠𝑖𝑜𝑛(𝑥 𝑘 (1) , 𝑥 𝑘 (2) ) = 𝑤𝑥 𝑘 (1) + (1 − 𝑤)𝑥 𝑘 (2) + 𝑛 𝑘 , 0 ≤ 𝑤 ≤ 1 , 𝑛 𝑘 ~𝑁(0, 𝜎 2 ), 𝑖𝑓 𝐹𝑢𝑠𝑖𝑜𝑛: 𝐾𝑎𝑙𝑚𝑎𝑛 The resulted point set using Eq. ( 33) is illustrated in Fig.…”

Section: Comparison Of Ultra-fusion With Other Fusion Methodsmentioning

confidence: 99%

“…I nformation generation using remotely sensed (RS) data and especially satellite images is increasing yearly due to the many benefits of them, such as permanent and continuous reception of data, wide geographical coverage, regular review and tracking, their availability and low cost, their acceptable radiometric and spatial resolution, etc. [1][2][3]. On the other hand, segmentation, defined as the categorization of image pixels into homogeneous regions, whereas each of them corresponds to a particular land-cover type, is an important processing step in many applications and analysis related to RS science such as anomaly, change, object, and target detection, classification, automatic pictorial pattern recognition, understanding plant/land distribution, and target tracking [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Ultrafusion: Optimal Fuzzy Fusion in Land-Cover Segmentation Using Multiple Panchromatic Satellite Images

Mahdipour,

Sharifi,

Sookhak

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Handling and quantifying two types of uncertainties, spatial and inherent, in land-cover segmentation using multiple satellite images constitutes a primary concern within the domain of segmenting multiple remote sensing images. Despite the comprehensive examination of the spatial uncertainty, the lack of a mathematical model addressing the inherent uncertainty in satellite images is notable. This paper endeavors to address this gap by considering and focuses on the latter. Leveraging multiple high-resolution panchromatic remote sensing (HR-PRS) images as input data, the study aims to model all procedures in the image formation and preprocessing stages (applied to each input image before segmentation) as the producing factors of inherent uncertainty in the segmentation process. The input images, conceptualized as events in random processes, undergo fusion using the mean estimator, referred to as the proposed "ultrafusion" method in this paper. The study demonstrates that the results of ultra-fusion can be effectively modeled by normal-type fuzzy numbers. The parameters of these fuzzy numbers are estimated using the maximum likelihood method. Subsequently, Fuzzy C-Means (FCM), as the most renowned clustering method, is employed for segmentation. Analytical comparisons reveal the notable performance of the proposed algorithm in terms of accurate segmentation and computational efficiency. Affirming the efficacy of the proposed approach, simulation results validate the advantages by improving the Overall Accuracy, Kappa, and F1-Score indices by about 0.86, 0.52, and 1.03 percent respectively, in compared to the most recent and similar method.

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Section: Comparison Of Ultra-fusion With Other Fusion Methodsmentioning

confidence: 99%

Section: Comparison Of Ultra-fusion With Other Fusion Methodsmentioning

confidence: 99%

Section: Comparison Of Ultra-fusion With Other Fusion Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ultrafusion: Optimal Fuzzy Fusion in Land-Cover Segmentation Using Multiple Panchromatic Satellite Images

Mahdipour,

Sharifi,

Sookhak

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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show abstract

“…Several studies have been conducted on hyperspectral remote sensing and inversion models. For instance, Felegari et al, mapped the Cd concentration and introduced the most suitable regression models, including support vector regression (SVR), partial least square regression (PLSR), and artificial neural networks (ANN) [ 70 ]. A Wavelet-Attention convolutional neural network (WA-CNN), Random forest (RF) and support vector machine (SVM) algorithms were utilized to automatically map the crops over the agricultural lands [ 71 ].…”

Section: Introductionmentioning

confidence: 99%

Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices

Zununjan,

Turghan,

Sattar

et al. 2024

Plant Methods

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Leaf water content (LWC) is a vital indicator of crop growth and development. While visible and near-infrared (VIS–NIR) spectroscopy makes it possible to estimate crop leaf moisture, spectral preprocessing and multiband spectral indices have important significance in the quantitative analysis of LWC. In this work, the fractional order derivative (FOD) was used for leaf spectral processing, and multiband spectral indices were constructed based on the band-optimization algorithm. Eventually, an integrated index, namely, the multiband spectral index (MBSI) and moisture index (MI), is proposed to estimate the LWC in spring wheat around Fu-Kang City, Xinjiang, China. The MBSIs for LWC were calculated from two types of spectral data: raw reflectance (RR) and the spectrum based on FOD. The LWC was estimated by combining machine learning (K-nearest neighbor, KNN; support vector machine, SVM; and artificial neural network, ANN). The results showed that the fractional derivative pretreatment of spectral data enhances the implied information of the spectrum (the maximum correlation coefficient appeared using a 0.8-order differential) and increases the number of sensitive bands, especially in the near-infrared bands (700–1100 nm). The correlations between LWC and the two-band index (RVI1156, 1628 nm), three-band indices (3BI-3(766, 478, 1042 nm), 3BI-4(1129, 1175, 471 nm), 3BI-5(814, 929, 525 nm), 3BI-6(1156, 1214, 802 nm), 3BI-7(929, 851, 446 nm)) based on FOD were higher than that of moisture indices and single-band spectrum, with r of − 0.71**, 0.74**, 0.73**, − 0.72**, 0.75** and − 0.76** for the correlation. The prediction accuracy of the two-band spectral indices (DVI(698, 1274 nm) DVI(698, 1274 nm) DVI(698, 1274 nm)) was higher than that of the moisture spectral index, with R2 of 0.81 and R2 of 0.79 for the calibration and validation, respectively. Due to a large amount of spectral indices, the correlation coefficient method was used to select the characteristic spectral index from full three-band indices. Among twenty seven models, the FWBI-3BI− 0.8 order model performed the best predictive ability (with an R2 of 0.86, RMSE of 2.11%, and RPD of 2.65). These findings confirm that combining spectral index optimization with machine learning is a highly effective method for inverting the leaf water content in spring wheat.

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ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012–2023 with Landsat series data

Singh,

Dahiya,

Sood

et al. 2024

Environ Monit Assess

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Using Experimental Models and Multitemporal Landsat-9 Images for Cadmium Concentration Mapping

Cited by 7 publications

References 21 publications

Ultrafusion: Optimal Fuzzy Fusion in Land-Cover Segmentation Using Multiple Panchromatic Satellite Images

Ultrafusion: Optimal Fuzzy Fusion in Land-Cover Segmentation Using Multiple Panchromatic Satellite Images

Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices

ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012–2023 with Landsat series data

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