Dataset Information

Prediction of soil salinity with soil-reflected spectra: A comparison of two regression methods.

ABSTRACT: To achieve the best high spectral quantitative inversion of salt-affected soils, typical saline-sodic soil was selected from northeast China, and the soil spectra were measured; then, partial least-squares regression (PLSR) models and principle component regression(PCR) models were established for soil spectral reflectance and soil salinity, respectively. Modelling accuracies were compared between two models and conducted with different spectrum processing methods and different sampling intervals. Models based on all of the original spectral bands showed that the PLSR was superior to the PCR; however, after smoothing the spectra data, the PLSR did not continue outperforming the PCR. Models established by various transformed spectra after smoothing did not continue showing superiority of the PCR over the PLSR; therefore, we can conclude that the prediction accuracies of the models were not only determined by the smoothing methods, but also by spectral mathematical transformations. The best model was the PCR based on the median filtering data smoothing technique (MF)?+?log (1/X)?+?baseline correction transformation (R2?=?0.7206 and RMSE?=?0.3929). To keep the information loss becoming too large, this suggested that an 8?nm sampling interval was the best when using soil spectra to predict soil salinity for both the PLSR and PCR models.

SUBMITTER: Zhang X

PROVIDER: S-EPMC6434016 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Prediction of soil salinity with soil-reflected spectra: A comparison of two regression methods.

Zhang Xiaoguang X Huang Biao B

Scientific reports 20190325 1

To achieve the best high spectral quantitative inversion of salt-affected soils, typical saline-sodic soil was selected from northeast China, and the soil spectra were measured; then, partial least-squares regression (PLSR) models and principle component regression(PCR) models were established for soil spectral reflectance and soil salinity, respectively. Modelling accuracies were compared between two models and conducted with different spectrum processing methods and different sampling interval ...[more]

PMID: 30911024

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Project description:ObjectivesTo explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis.MethodsA total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed. Twelve radiomics models were constructed using Lasso-Logistic or Lasso-Cox to evaluate and four annotation methods, 2D detailed annotation along tumour boundaries (2D), 3D detailed annotation along tumour boundaries (3D), 2D bounding box (2DBB), and 3D bounding box (3DBB) on T2-weighted images, were compared. Radiomics models were used to establish combined models incorporating clinical risk factors. The DeLong test was performed to compare the performance of models using the receiver operating characteristic curves.ResultsFor radiomics models, the area under the curve values ranged from 0.627 (0.518-0.728) to 0.811 (0.705-0.917) in the internal validation cohort and from 0.619 (0.469-0.754) to 0.824 (0.689-0.918) in the external validation cohort. Most radiomics models based on four annotations did not differ significantly, except between the 3D and 3DBB models for pLNM (p = 0.0188) in the internal validation cohort. For combined models, only the 2D model significantly differed from the 2DBB (p = 0.0372) and 3D models (p = 0.0380) for pDFS.ConclusionRadiomics and combined models constructed with 2D and bounding box annotations showed comparable performances to those with 3D and detailed annotations along tumour boundaries in rectal cancer characterisation and prognosis prediction.Critical relevance statementFor quantitative analysis of radiological images, the selection of 2D maximum tumour area or bounding box annotation is as representative and easy to operate as 3D whole tumour or detailed annotations along tumour boundaries.Key pointsThere is currently a lack of discussion on whether different annotation efforts in radiomics are predictively representative. No significant differences were observed in radiomics and combined models regardless of the annotations (2D, 3D, detailed, or bounding box). Prioritise selecting the more time and effort-saving 2D maximum area bounding box annotation.

Dataset Information

Prediction of soil salinity with soil-reflected spectra: A comparison of two regression methods.

Publications

Prediction of soil salinity with soil-reflected spectra: A comparison of two regression methods.

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